Dynamics and Simulation of a Snakeboard‟s Motion

Transcription

Dynamics and Simulation of a
Snakeboard‟s Motion
Jonathan Jamieson
Department of Mechanical and Aerospace
Engineering
Supervisor – Dr James Biggs
MEng Degree in Mechanical Engineering
April 2012
i
Acknowledgments
I would like to thank Dr James Biggs for agreeing to supervise this project
and pointing me in the right direction.
ii
Abstract
The aim of this paper was to investigate and understand the non-intuitive
dynamics of a snakeboard with a long-term vision of developing controls for
similar systems.
A numerical simulation of the four, coupled, non-linear differential equations
that represent the motion of the snakeboard was created using the MATLAB
coding environment. Sinusoidal, periodic controls were defined and by
varying the ratio of angular frequencies, the following gaits could be
analysed: resonant, rotation and parking.
Through experimentation with controlled parameters, it was found that the
direction of travel for the resonant and parking gait depended on the
amplitude relationship between the rotor and the platforms. The direction of
travel for the rotation gait depended solely upon the amplitude of the rotor.
The variation in behaviour caused by changing the amplitude and absolute
angular frequencies was also investigated. Larger angular frequencies
resulted in greater displacements but the change in behaviour using different
amplitudes was more complicated. A bifurcation study was also performed.
An open loop control method was developed so changes to controlled
parameters could be made in real time and their effects observed. To provide
more control, a physical input device was created to interface with a
computer running the simulation.
A numerical motion planning algorithm was developed to find the control
parameters which would displace the snakeboard from rest, for a specified
displacement in the 𝑥-direction in a given duration. It was found that precise
angular frequencies were needed for accurate displacement. Consequently,
the iterative process must refine the parameters being changed for a
reasonable computational time.
Finally, an algorithm that ensured stability in the resonance gait was
developed. The behaviour matched closely matched the author‟s experience
of riding snakeboard.
iii
List of Contents
Acknowledgments ...........................................................................................ii
Abstract .......................................................................................................... iii
List of Contents ..............................................................................................iv
List of Notations ........................................................................................... viii
1.
Introduction ............................................................................................. 1
1.1. Nonholomonic Systems .................................................................... 1
1.2. Background to Snakeboarding .......................................................... 3
2.
Literary Review ....................................................................................... 5
3.
Mathematical Model ................................................................................ 6
3.1. Nonholomonic Constraints ................................................................ 6
3.2. Controlled Parameters ...................................................................... 7
3.3. Physical Parameters ......................................................................... 7
3.4. ODE45 Differential Solver ................................................................. 7
3.5. Dynamical Equations of Motion ........................................................ 9
3.6. Validation ........................................................................................ 10
3.6.1.
Validation Test 1 ................................................................... 10
3.6.2.
Validation Test 2 ................................................................... 11
3.7. Limitations....................................................................................... 12
3.8. Plotting and Visualisation ................................................................ 13
3.8.1.
Static Overlay........................................................................ 13
3.8.2.
Animated 2D ......................................................................... 14
3.8.3.
Animated 3D ......................................................................... 14
3.9. MATLAB Code ................................................................................ 14
4.
Resonance Gait .................................................................................... 15
4.1. Direction of Travel ........................................................................... 16
4.2. Amplitude ........................................................................................ 17
iv
4.3. Angular Frequency ......................................................................... 19
4.4. Phase shift ...................................................................................... 20
4.5. Instability ......................................................................................... 21
5.
Rotation Gait ......................................................................................... 23
5.1. Direction.......................................................................................... 24
5.2. Rate of rotation ............................................................................... 25
6.
5.2.1.
Angular frequency ................................................................. 25
5.2.2.
Amplitude of platforms and rotors ......................................... 26
5.2.3.
Initial Velocity ........................................................................ 27
Parking Gait .......................................................................................... 28
6.1. Direction.......................................................................................... 28
6.2. Rate of change in the 𝒚-direction .................................................... 30
6.2.1.
Angular Frequency ................................................................ 30
6.2.2.
Amplitude of platforms and rotors ......................................... 31
6.3. Initial Velocity .................................................................................. 32
7.
Direct Simulation Controller .................................................................. 33
7.1. Limitations....................................................................................... 34
7.2. Construction.................................................................................... 35
7.2.1.
Components.......................................................................... 35
7.2.2.
Microcontroller ...................................................................... 35
7.2.3.
Final Design .......................................................................... 35
7.2.4.
Improvements ....................................................................... 36
7.3. Results ............................................................................................ 36
8.
7.3.1.
Validation Test ...................................................................... 37
7.3.2.
Resonance imitation test ....................................................... 38
7.3.3.
Rotation Imitation Test .......................................................... 40
Open loop periodic controls .................................................................. 42
v
9.
Bifurcation study ................................................................................... 44
9.1. Phase shift ...................................................................................... 44
9.1.1.
Rotor phase shift ................................................................... 44
9.1.2.
Platform phase shift .............................................................. 45
9.2. Angular Frequency ......................................................................... 46
10.
9.2.1.
Rotor angular frequency........................................................ 46
9.2.2.
Platform angular frequency ................................................... 47
Motion Planning ................................................................................. 49
10.1. Speeding up and slowing down ...................................................... 49
10.1.1.
Test 1 .................................................................................... 49
10.1.2.
Test 2 .................................................................................... 50
10.2. Parameter to be changed ............................................................... 51
10.2.1.
Rotor Amplitude Test ............................................................ 51
10.2.2.
Platform Amplitudes Test ...................................................... 52
10.2.3.
Velocity Test ......................................................................... 53
10.2.4.
Conclusions .......................................................................... 54
10.3. Algorithms ....................................................................................... 55
10.3.1.
Version 1 ............................................................................... 56
10.3.2.
Version 2 ............................................................................... 58
10.3.3.
Version 3 ............................................................................... 59
10.4. Improvements ................................................................................. 61
11.
Stable Resonance Gait ...................................................................... 62
12.
Conclusions ....................................................................................... 66
12.1. Summary ........................................................................................ 66
12.2. Evaluation ....................................................................................... 67
12.3. Further Work ................................................................................... 67
13.
References ......................................................................................... 68
vi
Appendix I
– Timestep .............................................................................. 1
Appendix II
– MATLAB Code ..................................................................... 3
Appendix III
– Microcontroller Code .......................................................... 26
vii
List of Notations
𝐽𝑏
–
Moment of inertia of crossbar (kg·m²)
𝐽𝑝
–
Moment of inertia of platforms (kg·m²)
𝐽𝑟
–
Moment of inertia of rotor (kg·m²)
𝑎𝑏
–
Amplitude of rotation for the back platform (radians)
𝑎𝑓
–
Amplitude of rotation for the front platform (radians)
𝑎𝑟
–
Amplitude of rotation for the rotor (radians)
𝛽𝑏
–
Phase change of the front platform (radians)
𝛽𝑓
–
Phase change of the front platform (radians)
𝛽𝑟
–
Phase change of the rotor (radians)
𝜑𝑏
–
The back platform‟s angle of rotation with respect to the centre line
of the snakeboard (radians)
𝜑𝑓
–
The front platform‟s angle of rotation with respect to the centre line
of the snakeboard (radians)
𝜔𝑏
–
Angular frequency of the back platform (radian/second)
𝜔𝑓
–
Angular frequency of the front platform (radian/second)
𝜔𝑟
–
Angular frequency of the rotor (radian/second)
𝐽
–
Moment of inertia of crossbar and platforms (kg·m²)
𝑉
–
Velocity of the snakeboard‟s centre of mass (m/s)
𝑙
–
The half length of the snakeboard (m)
𝑚
–
Total mass of the system (kg)
𝑥, 𝑦 –
Coordinates of snakeboard‟s centre of mass (m)
𝛿
–
The rotor‟s angle of rotation with respect to the centre line of the
snakeboard (radians)
𝜃
–
The angle between the 𝑥-axis and the central line of the
snakeboard (radians)
𝜔
–
Angular frequency of platforms and rotor (radian/second)
viii
1.
Introduction
1.1.
Nonholomonic Systems
A nonholomonic system is one whose state is dependent on the trajectory
(path) performed to reach that position. The parameters which control the
system, described by nonholomonic constraints, may begin at a set of
values, change continuously (usually over time) and return to their original
values without the system having returned to its original state.
A good, qualitative comparison to illustrate the difference between a
nonholomonic system and a holomonic one is a sack trolley (a two wheeled
device for carrying heavy loads, Figure 1) and a stationary robotic arm with
two pivot joints (Figure 2).
Wheel
Rotation
(radians)
Right wheel
Left wheel
𝑦
Initial position
𝑂
Final position
Time (seconds)
𝑥
Figure 1 – Nonholomonic system (sack trolley)
Pivot
Rotation
(radians)
Pivot 1
Pivot 2
𝑦
Initial position and final position
𝑂
𝑥
Figure 2 – Holomonic system (robotic arm)
–1–
Time (seconds)
In the case of the holomonic system, when the initial configuration occurs the
system always returns to its original position. In contrast, when the
nonholomonic system is returned to its original configuration (wheels at zero
rotation) it does not have to return to its original position.
Nonholomonic kinematics cannot be described by algebraic equations that
relate the parameters of the system (such as wheel rotation) with the
absolute position and orientation of it. Instead, differential relationships must
be used.
This has implications regarding system control and implementing motion
planning. As the complexity of robotics increases, the desire to automate
tasks normally carried out by humans grows stronger. However, from the
sack trolley example, it becomes apparent that this is not a trivial task
because there are an infinite number of paths the trolley can travel.
Humans have an intuitive understanding of the optimal path and can react to
changing circumstances. For example, if a pet walked in front of the trolley
the person pushing it would automatically avoid the new obstacle and
calculate a different route. This is a significant challenge for a robot and
although great advances have been made, artificial intelligence in this area is
below that of a human.
Many robots that mimic nature (like robotic fish and snakes) are subject to
nonholomonic constraints. Research has been carried out on animals with
the aim of improving the motion of their robotic counterparts.
Unlike many nonholomonic systems that could be turned into robotic form,
the snakeboard does not have a direct biological counterpart. The author
chose to analyse snakeboards because they are an interesting example of
nonholomonic motion and his personal interest in the sport.
–2–
1.2.
Background to Snakeboarding
The snakeboard was invented in 1989 by James Fisher and Oliver Macleod
Smith (Figure 3). The popularity of snakeboarding (the sport of riding a
snakeboard) peaked in the 1990‟s, however, it has continued in different
forms and names such as “streetboarding”
Figure 3 – Image from Patent Number: 4955626 relating to snakeboard
At first glance an observer would note the similarity between the snakeboard
and a regular skateboard, however, the snakeboard moves with a strange
snake-like motion (hence the name). Other differences also become
apparent, for example, the rider need not put his foot down on the ground to
propel himself. The author‟s interest was piqued at an early age by the sight
of riders seemingly defying gravity by riding uphill without pushing their feet
against the ground.
Instead of pushing, a snakeboarder will gain momentum by coupling the
twisting of the torso and rotations of the feet. This is made possible by the
special construction of the snakeboard. Similar in size to the skateboard, it
also has two small wheels at either end. The key difference is that the
platforms upon which the rider stand are connected to the rigid crossbar with
revolute joints that allow the trucks (the axle units where the wheels are
attached) to rotate through the vertical axis. Figure 4 is a labelled photograph
of a snakeboard and the features previously described.
–3–
Crossbar
Wheel
Revolute joint
Trucks
Platform
Figure 4 – Labelled photograph of a snakeboard
The beginner will struggle to attain any movement along the ground because
the motions required of the body and feet are unintuitive. To move the torso
must be rotated back and forth while simultaneously turning their feet so they
point in (Figure 5) and then out (Figure 6).
Figure 5 – Feet pointing in
Figure 6 – Feet pointing out
–4–
2.
Literary Review
Academic interest in snakeboards began in 1995 with a mathematical model
created by Andrew Lewis and James Ostrowski et al [1]. Various
simplifications were made, including the assumption that the platforms rotate
in equal and opposite directions. They also demonstrated the three basic
gaits analysed in this paper.
Prior to this, it was recognised that sinusoids could be used to control
nonholomonic systems and Richard Murray and S. Shankar investigated this
in 1993 [2]. This proved to be a starting point for others such as Stefano
Iannitti and Kevin Lynch to develop motion planning methods for the
snakeboard [3]. An analytical method for motion planning of a snakeboard
was presented by Andrew Lewis and F. Bulllo in 2004 [4].
Other academic work regarding snakeboards includes that of A. Asnafi and
M Mahzoon to develop interesting and flower-like gaits for the snakeboard
[5].
Various attempts have been made to create a robotic snakeboard but the
author could not find any well-documented examples except that of
undergraduate electrical and control engineering student, Eddy Veltman [6].
A.S Kuleshov improved the mathematical model proposed in [1] by:
“[taking] into account an opportunity that platforms of a snakeboard
can rotate independently from each other”
In his paper [7], he creates equations of motion derived in the Gibbs-Appell
form that describe the motion of the snakeboard.
–5–
3.
Mathematical Model
To investigate the dynamics of snakeboard motion a mathematical model
was created. Figure 7 represents how the snakeboard is modelled in this
paper. It is assumed that it can only move in the 𝑥𝑦 plane and that 𝑂𝑥𝑦 is a
fixed coordinate system. Point 𝐴, the centre of mass of the system, is given
coordinates 𝑥 and 𝑦. The angle between the 𝑥-axis and the central line of the
snakeboard is denoted by 𝜃. The front and back platforms can rotate
independently and their angles relative to the central line of the snakeboard
are denoted by 𝜑𝑓 and 𝜑𝑏 respectively. The rider is simplified to a rotor in the
centre of the snakeboard. The rotor‟s angle of rotation with respect to the
centre line is denoted by 𝛿.
𝜑𝑓
𝛿
𝐴
𝜃
𝜑𝑏
𝑦
𝑂
𝑥
Figure 7 – Mathematical representation
3.1.
Nonholomonic Constraints
It is assumed that the wheels may only roll without slipping on the 𝑥𝑦 plane.
This restriction is a nonholomonic constraint and takes the form of Equation
1 for the front platform and Equation 2 for the back platform.
0 = − sin 𝜑𝑓 + 𝜃 𝑥 + cos 𝜑𝑓 + 𝜃 𝑦 + 𝑙 𝑐𝑜𝑠(𝜑𝑓 )𝜃
Equation 1
0 = − sin 𝜑𝑏 + 𝜃 𝑥 + cos 𝜑𝑏 + 𝜃 𝑦 − 𝑙 𝑐𝑜𝑠(𝜑𝑏 )𝜃
Equation 2
–6–
3.2.
Controlled Parameters
There are three controlled parameters in this system: 𝜑𝑓 , 𝜑𝑏 and 𝛿. By
performing periodic loops with the controlled parameters, different gaits can
be achieved. Gaits are defined as “a generator of motion in a certain
direction”[1] . To perform these periodic loops three equations as functions of
time were created for 𝜑𝑓 , 𝜑𝑏 and 𝛿 (Equation 3, Equation 4 and Equation 5
respectively):
𝜑𝑓 𝑡 = 𝑎𝑓 sin(𝜔𝑓 𝑡 + 𝛽𝑓 )
Equation 3
𝜑𝑏 𝑡 = 𝑎𝑏 sin(𝜔𝑏 𝑡 + 𝛽𝑏 )
Equation 4
𝛿 𝑡 = 𝑎𝑟 sin(𝜔𝑟 𝑡 + 𝛽𝑟 )
Equation 5
The control parameters 𝑎𝑓 , 𝑎𝑏 and 𝑎𝑟 determine the amplitude of rotation
whilst the frequencies are determined by 𝜔𝑓 , 𝜔𝑏 and 𝜔𝑟 . Each controlled
parameter can be shifted in phase using the respective control
parameter 𝛽𝑓 , 𝛽𝑏 and 𝛽𝑟 .
The periodic loops are used in all sections of this paper except the physical
input controller where the controlled parameters are taken directly.
3.3.
Physical Parameters
Table 1 lists the values of parameters used in this investigation. They
represent the physical properties of a snakeboard. These were constant
throughout the paper for every simulation.
Parameter
Value
l
m
J
Jr
Jp
0.285 m
75 kg
0.22 kg m2
14 kg m2
0.013 kg m2
Table 1 – Physical Parameters
3.4.
ODE45 Differential Solver
The equations of motion for the snakeboard are ordinary differential
equations. To solve these, the MATLAB ODE45 function was used. The
ODE45 solver is recommended for nonstiff problem types and has a good
–7–
order of accuracy. It requires the initial conditions, for this problem 𝑥, 𝑦, 𝜃
and 𝑉, in vector form. There are two outputs, a column vector of time points
and a solution array with rows corresponding to the solution at the relevant
time point.
For this investigation, the position and velocity of the snakeboard between
the initial and final solution are of interest. This is because the motion is
unintuitive so it is beneficial for the interim positions to be known. Because
the ODE45 solver is an adaptive integrator, only an initial time and final time
are required. However, to obtain an output that describes the full motion a
timestep can be applied. Small timesteps will give smooth trajectories in
contrast to large timesteps where the trajectory of the snakeboard is unclear.
It should be emphasised that decreasing the timestep has no appreciable
effect on the accuracy of the final solution (see Table 2) and the
computational time is only increased because more timesteps need to be
stored in the computer‟s memory.
A test was conducted were everything remained constant except timestep
with the results shown in Table 2. The final 𝑥 position was recorded to prove
accuracy was unaffected and the solution time for the integration was
measured to check if there were any significant differences in computational
time.
Timestep (s) ODE Solution time (s) Final 𝒙 position (m)
5
2.5
1
0.5
0.25
0.1
0.05
0.01
0.3183
0.3373
0.3329
0.3339
0.3357
0.3470
0.3569
0.4085
0.9608
0.9608
0.9608
0.9609
0.9608
0.9609
0.9608
0.9608
Table 2 – ODE 45 Timestep
Table 2 confirms that there is no increase in accuracy when time step is
reduced but the solution time increases. Therefore, a qualitative approach
was taken. Trajectory plots for each timestep were created (see Appendix I –
Timestep) and it was decided that 0.1s was the largest possible timestep to
give satisfactory results.
–8–
3.5.
Dynamical Equations of Motion
The system of equations derived for the motion of a snakeboard derived in
[7] was rearranged into four, coupled, non-linear differential equations
(Equation 6, Equation 7, Equation 8 and Equation 9) that were in a form
suitable for MATLAB to solve.
𝑥 = 𝑉 cos 𝜃 −
𝑉 sin 𝜓2 𝑠𝑖𝑛 𝜃
𝑐𝑜𝑠𝜓1 + 𝑐𝑜𝑠𝜓2
Equation 6
𝑦 = 𝑉 sin 𝜃 +
𝑉 sin 𝜓2 𝑐𝑜𝑠 𝜃
𝑐𝑜𝑠𝜓1 + 𝑐𝑜𝑠𝜓2
Equation 7
𝜃=
−
𝑉=
𝑉 sin 𝜓1
𝑙 (cos 𝜓1 + cos 𝜓2 )
(𝑑1 𝛿 + 𝑑2 𝜓2 ) sin 𝜓1
− 𝑉 × 𝑀(𝑡)
(cos 𝜓1 + cos 𝜓2 )
sin2 𝜓2 + 𝑘 2 sin2 𝜓1
1+
cos 𝜓1 + cos 𝜓2 2
Equation 8
Equation 9
Where:
𝑀 𝑡 =
𝜓2 sin 𝜓2 cos 𝜓2 + 𝑘 2 𝜓1 sin 𝜓1 cos 𝜓1
+
𝜓1 sin 𝜓1 𝜓2 sin 𝜓2
(sin2 𝜓2 + 𝑘 2 sin2 𝜓1 )
Equation 10
𝐽 + 𝐽𝑟 + 2𝐽𝑝
𝑚𝑙 2
Equation 11
𝑑1 =
𝐽𝑟
𝑚𝑙
Equation 12
𝑑2 =
𝐽𝑝
𝑚𝑙
Equation 13
𝑘2 =
𝜓1 = 𝜑𝑓 − 𝜑𝑏
Equation 14
𝜓2 = 𝜑𝑓 + 𝜑𝑏
Equation 15
–9–
3.6.
Validation
To validate the mathematical model had been correctly entered into MATLAB
two of the results from [7] were replicated.
The method of applying controlled parameters is slightly different from the
one used in this investigation. Instead of having the individual functions for
each platform with respect to time, they are merged together (Equation 14
and Equation 15). This method is limited because it is harder to define the
periodic controls. Most of the other papers use a similar method to the one
used in the rest of this paper.
3.6.1.
Validation Test 1
This case was labelled as an example of a “nonresonant case” which is
representative of beginners unable to ride a snakeboard. There is only a
small displacement in the 𝑥-direction and no appreciable forward motion. The
controlled parameters are given in Table 3 and the trajectory calculated by
the MATLAB model used for this paper is shown in Figure 9. Figure 8 is the
trajectory plotted in [7]. The trajectories are similar, both quantitatively and
qualitatively.
Controlled Parameter
Function
0.3 sin
1
𝑡
3
1
0.2 sin 𝑡
2
1
0.7 sin
𝑡
2
𝜓1 𝑡
𝜓2 (𝑡)
𝛿(𝑡)
Table 3 – Validation Test 1 parameters
Figure 8 – Trajectory of snakeboard in Validation Test 1 from [7]
– 10 –
Figure 9 – Trajectory of snakeboard in Validation Test 1 using MATLAB model in this paper
3.6.2.
Validation Test 2
This test was similar to the previous one but used a resonant gait. The
controlled parameters are given in Table 4 and the trajectory calculated by
the MATLAB model used for this paper is shown in Figure 10. Figure 11 is
the trajectory plotted in [7].
Controlled Parameter
Function
0.3 sin
1
𝑡
3
1
0.2 sin 𝑡
2
1
0.7 sin
𝑡
2
𝜓1 𝑡
𝜓2 (𝑡)
𝛿(𝑡)
Table 4 – Validation Test 1 parameters
Figure 10 – Trajectory of snakeboard in
Validation Test 1 using MATLAB model in this
paper
– 11 –
Figure 11 – Trajectory of snakeboard in
Validation Test 1 from [7]
Again, the trajectories are both similar quantitatively and qualitatively. From
these two tests, it was concluded that the MATLAB model used in this paper
replicated the results of previous work sufficiently well and validated the
model.
3.7.
Limitations
The major limitation of the dynamic equations used in this paper is friction is
not taken into account. From the author‟s experience of riding a snakeboard,
this is unrealistic, especially at lower speeds.
An example of the lack of realism from neglecting friction is when [7]
concludes that:
Even if the rider doesn’t rotate his torso, he can propel [the]
snakeboard forward using only his legs
This may be true in an idealised world but an engineer designing for practical
applications would be unable to achieve motion without a rotor of a
reasonable moment of inertia.
An experienced rider can shift their body weight allowing for tight turns at
high speeds, however, there is a limit to what is possible without the rider
falling off. This simulation assumes that the rider cannot fall off or the robot
remains stable at all times. If a robotic snakeboard was being designed then
the centre of mass of the rotor should be as low as possible to minimise the
chance of tipping.
This model also assumes that the surface the snakeboard is perfectly
smooth and level. In reality, a gradient always exists and the surface
undulates. Future work could take into account these effects but it would add
a significant level of complexity onto the model.
One phenomenon observed by the author when riding downhill at high
speeds is “speed wobble”. The snakeboard platforms begin to oscillate
rapidly and uncontrollably. However, this is outside the scope of this model
and study.
– 12 –
3.8.
Plotting and Visualisation
The majority of the figures in this paper are line graphs because they are the
most suitable. However, other methods were created to represent the motion
of the snakeboard over time. These were useful because the snakeboard‟s
motion can be unintuitive so it is often difficult to understand a line plot of its
trajectory.
Static Overlay
3.8.1.
An example of the plot defined as “Static Overlay” for the purposes of this
paper is shown in Figure 12. Two-dimensional snapshots of the
snakeboard‟s position and state are taken at fixed intervals and overlaid to
form a single image.
Figure 12 – Static Overlay visualisation example
An indication of the snakeboard‟s velocity can be observed from the
separation between each snapshot; a large displacement corresponds to a
large velocity. The main problem with this visualisation is when snapshots
overlap, so distinguishing between them becomes difficult.
– 13 –
3.8.2.
Animated 2D
This visualisation is similar to the previous one but instead of overlaying each
snapshot, they are shown sequentially. This makes it unsuitable for a printed
medium but an example is shown in Figure 13.
Figure 13 – Animated 2D visualisation example
3.8.3.
Animated 3D
This visualisation is a 3D version of the “Animated 2D” version. An example
is shown in Figure 14. The camera position could be fixed in space or move
relative to the snakeboard‟s position and angle.
Figure 14 – Animated 3D visualisation example
3.9.
MATLAB Code
The MATLAB code written by the author to produce the results in this paper
can be found in “Appendix II – MATLAB Code.”
– 14 –
4.
Resonance Gait
Resonance is defined as when the platforms and rotor oscillate at the same
frequency, that is, when 𝜔𝑓 , 𝜔𝑏 and 𝜔𝑟 are at a ratio of 1:1:1 and there is no
phase shift (𝛽𝑓 = 𝛽𝑏 = 𝛽𝑟 = 0). It is the gait used by snakeboarders and
produces a net displacement in the 𝑥-direction. The amplitudes 𝑎𝑓 and 𝑎𝑏
must be in opposite directions (but not necessarily equal magnitude) for
motion to occur.
As an example of resonance gait, Figure 15 shows the trajectory of the
snakeboard when the simulation is run with the control parameters listed in
Table 5. The controlled parameters over time are shown in Figure 16.
Parameter
Value
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝜔𝑓
𝜔𝑏
𝜔𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
0.7 rad
1 rad/s
1 rad/s
1 rad/s
0 rad
0 rad
0 rad
Table 5 – An example of resonance gait control parameters.
Figure 15 – Example of resonance trajectory
Figure 16 – Controlled parameters during resonance
– 15 –
Direction of Travel
4.1.
The parameters in Table 6 were kept constant while various combinations of
platform amplitudes were tested. The results of the tests are shown in Table
7. It can been seen that when the direction of 𝑎𝑓 and 𝑎𝑟 are the same the
snakeboard accelerate in the direction of the front platform. Likewise, when
𝑎𝑏 and 𝑎𝑟 are the same the snakeboard will accelerate in the direction of the
back platform.
Parameter
Value
𝜔𝑓
𝜔𝑏
𝜔𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
1 rad/s
1 rad/s
1 rad/s
0 rad
0 rad
0 rad
Table 6 – Direction of travel during resonance gait, constant parameters
𝒂𝒇 (rad)
𝒂𝒃 (rad)
0.3
0.3
-0.3
-0.3
-0.3
-0.3
0.3
0.3
𝒂𝒓 (rad) 𝒙-displacement Trajectory
-0.7
0.7
0.7
-0.7
Negative
Positive
Negative
Positive
Figure 17
Figure 18
Figure 19
Figure 20
Table 7 – Direction of travel for resonance gait
Figure 17
Figure 18
Figure 19
Figure 20
– 16 –
4.2.
Amplitude
To gain an understanding of how the amplitude of the rotor and platforms
affected the motion of the snakeboard, surface plots were created. The
distance travelled in the 𝑥-direction was calculated for various time periods.
For each calculation, the platform amplitudes were assumed to have equal
magnitudes in opposite directions (𝑎𝑓 = −𝑎𝑏 ). The platform and rotor
amplitudes were varied from 0 to 1.5 radians in increments of 0.01 radians.
The maximum limit of 1.5 radians was chosen because rotation greater than
90 degrees in the platforms will cause the snakeboard to exhibits strange
behaviour and the gait is not resonant. The parameters in Table 8 were kept
constant and the results are listed in Table 9.
Parameter
Value
𝜔𝑓
𝜔𝑏
𝜔𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
1 rad/s
1 rad/s
1 rad/s
0 rad
0 rad
0 rad
Table 8 – Constant parameters during resonance amplitude tests
Simulation
Time (s)
Platform
amplitude
associated
with largest 𝒙displacement
Rotor
amplitude
associated
with largest 𝒙displacement
Largest 𝒙displacement
Surface
Plot
10
30
70
120
0.53
0.50
0.37
0.29
1.5
1.5
1.5
1.5
0.69
5.80
25.4
60.27
Figure 21
Figure 22
Figure 23
Figure 24
Table 9 – Amplitude surface plot results
Figure 21 – Amplitude surface plot for 10
seconds
seconds
– 17 –
seconds
seconds
From Table 9 it can be seen that the greater the largest rotor amplitude
always gives the largest 𝑥-displacement. However, when starting from rest, if
large platform amplitudes are used for long times then the oscillations of the
snakeboard in the 𝑦-direction grow very large. This is best explained by the
trajectory shown in Figure 25. The large oscillations in the 𝑦-direction soon
become unstable and the snakeboard doubles back on itself meaning the
displacement in the 𝑥-direction is reduced. The concept of stability in the
resonance gait is investigated in section 4.5 titled “Instability”
Figure 25 – Trajectory during resonance gait with large platform amplitudes
For short run times a large platform amplitude is desirable because it gets
the snakeboard moving. Optimising the platform amplitudes to maximise
displacement in the 𝑥-direction is investigated further in section 11 titled
“Stable Resonance Gait”.
– 18 –
4.3.
Angular Frequency
The effect of changing the angular frequency of the platforms and rotor was
investigated in a similar way to that of the amplitude. The angular
frequencies 𝜔𝑓 , 𝜔𝑏 and 𝜔𝑟 were varied from 0 to 2 rad/s in increments of
0.05 rad/s. The parameters in Table 10 were kept constant and a run time of
200 seconds for each combination was used. A surface plot of the results is
shown in Figure 26.
Parameter
Value
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
0.7 rad
0 rad
0 rad
0 rad
Table 10 – Constant parameters during resonance gait, angular frequency test
Figure 26 – Surface plot of angular velocities
This plot shows that there is no other combination of angular frequencies
apart from resonance that produces a significant net displacement in the 𝑥direction. It also indicates how sensitive resonance is. If the ratio of platforms
and rotors is not extremely close to 1:1:1 then its associated gait is not
achieved.
The small “humps” that are visible occur when 𝜔𝑓 , 𝜔𝑏 and 𝜔𝑟 are at a ratio of
1:1:2. This ratio produces the rotation gait and is discussed in section 5.
– 19 –
Assuming the ratio of 𝜔𝑓 , 𝜔𝑏 and 𝜔𝑟 is 1:1:1 then the greater they are the
further the snakeboard will travel in a given period until instability occurs
(Figure 27).
Figure 27 – Displacement during a range of angular frequencies
There is an exponential relationship between angular frequency and the
displacement travelled in the 𝑥-direction until 𝜔 is greater than 2.25 rad/s in
this case. The reason it does not continue to grow is that the gait becomes
unstable.
For motion planning care needs to be taken that if the angular frequencies
were increased to travel that the gait does not become unstable if long
runtimes are used.
4.4.
Phase shift
Another surface plot (Figure 28) was created to investigate the effect of
changing the phase shift of the platforms and rotors. The phase shift of the
rotor and the back platform (𝛽𝑟 and 𝛽𝑏 ) was varied from 0 to 4𝜋 radians in
increments of 0.05 radians. The simulation run time was 50 seconds for each
combination and the parameters in Table 11 were kept constant.
Parameter
Value
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝜔𝑓
𝜔𝑏
𝜔𝑟
0.3 rad
-0.3 rad
0.7 rad
1 rad/s
1 rad/s
1 rad/s
Table 11 – Constant parameters for phase shift test
– 20 –
Figure 28 – Surface plot of the effects of phase shift
The surface plot confirms the conclusions made about the direction of
motion. A phase shift of 2𝜋 radians is equivalent to reversing the direction of
the amplitudes.
4.5.
Instability
For simulations with long run times, the snakeboard‟s trajectory becomes
unstable. This was noted in [1]:
“one cannot just increase the run time and input magnitude to get
longer distances traveled in the (1,1,1) [resonance] gait”
Figure 29 shows the trajectory of the snakeboard during a resonance
simulation with a long run time and Figure 30 is a close-up of the unstable
part. Unlike the previous example of instability (Figure 25), more reasonable
platform amplitudes were used (𝑎𝑓 = −𝑎𝑏 = 0.3) and instability still occurs,
albeit longer into the run time.
Although efforts were made to determine a method of predicting when
instability would occur this was not achieved. Instead, checks can be made
after the trajectory has been calculated to ensure the snakeboard has not
travelled backwards.
– 21 –
Figure 29 – Trajectory becoming unstable
Figure 30 – Close-up of where trajectory becomes unstable
– 22 –
5.
Rotation Gait
The rotation gait was discovered first in [1] and occurs when the angular
frequencies 𝜔𝑓 , 𝜔𝑏 and 𝜔𝑟 are at a ratio of 1:1:2. It generates a net motion in
the 𝜃-direction and when combined with an initial non-zero velocity, circular
motion is produced. Snakeboard riders do not use this method to turn;
instead, they slightly adapt the resonance gait to correct the direction they
wish to go. Nonetheless, robotic snakeboards would not struggle to use this
gait so it was analysed.
As an example of the rotation gait, Figure 45 shows the trajectory of the
Table 12. The controlled parameters over time are shown in Figure 45.
Parameter
Value
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝜔𝑓
𝜔𝑏
𝜔𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
0.7 rad
1 rad/s
1 rad/s
2 rad/s
0 rad
0 rad
0 rad
Table 12 – An example of
resonance gait control parameters
Figure 31 – Trajectory of snakeboard in rotation gait
Figure 32 – Controlled parameters during rotation gait
– 23 –
Direction
5.1.
The first concern was to understand how to control the direction of the
rotation. From the work done in section 4 on resonance it seemed likely that
the directions of 𝑎𝑓 , 𝑎𝑏 and 𝑎𝑟 would influence the direction of rotation.
Just as no movement occurs when the platform amplitudes are in the same
direction during the resonance gait, the same is true for the rotation gait.
Consequently, there were four combinations of amplitude directions and
these were each tested in four separate simulations.
The parameters in Table 13 were kept constant for each of the four
simulations. The simulation run time was fixed at 25 seconds and the results
are shown in Table 14.
Parameter
Value
𝜔𝑓
𝜔𝑏
𝜔𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
1 rad/s
1 rad/s
2 rad/s
0 rad
0 rad
0 rad
Table 13 – Constant parameters during rotation direction test
𝒂𝒇 (rad)
𝒂𝒃 (rad)
𝒂𝒓 (rad)
Rotation Direction
Trajectory
0.3
0.3
-0.3
-0.3
-0.3
-0.3
0.3
0.3
-0.7
0.7
0.7
-0.7
Clockwise
Counter-clockwise
Counter-clockwise
Clockwise
Figure 33
Figure 34
Figure 35
Figure 36
Table 14 – Results of rotation direction test
Figure 33
Figure 34
– 24 –
Figure 35
Figure 36
The results in Table 14 show that as long as the amplitudes of the platforms
are opposite in direction, it is the direction 𝑎𝑟 that determines the direction of
rotation. A positive value for 𝑎𝑟 causes a counter-clockwise rotation and a
negative value for 𝑎𝑟 causes clockwise rotation.
Rate of rotation
5.2.
The rate of rotation can be changed by varying the amplitudes or angular
frequencies of the controlled parameters. Both of these were investigated in
the following sections.
5.2.1.
Angular frequency
To investigate the effect of angular frequency a script was created that varied
𝜔𝑓 and 𝜔𝑏 from 0 to 2 in increments of 0.1 radians while keeping a ratio of:
𝜔𝑟 = 2 𝜔𝑓 = 2 𝜔𝑓 . Each iteration was run for 25 seconds and the angle of
rotation, 𝜃 was recorded and plotted (Figure 37). The parameters in Table 15
were kept constant. It can be seen that the relationship between rate of
angular rotation and the angular frequencies is linear.
Parameter
Value
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
-0.3 rad
0 rad
0 rad
0 rad
Table 15 – Control parameters for rate of
rotation, angular frequency test
Figure 37 – Rate of rotation for a range of
angular frequencies
– 25 –
5.2.2.
Amplitude of platforms and rotors
The first amplitude investigation was changing 𝑎𝑟 from 0 to 0.7 radians in
increments of 0.01 radians. Each iteration was run for 25 seconds and the
angle of rotation, 𝜃 was recorded and plotted (Figure 38). The parameters in
Table 16 were kept constant. The relationship between the rate of angular
rotation and amplitude of the rotor rotation is linear.
Parameter
Value
𝑎𝑓
𝑎𝑏
𝜔𝑓
𝜔𝑏
𝜔𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
1 rad/s
1 rad/s
2 rad/s
0 rad
0 rad
0 rad
Table 16 – Constant parameters for
rate of rotation rotor amplitude test
Figure 38 – Rate of rotation for a range of rotor
amplitudes
The second amplitude investigation was changing 𝑎𝑓 and 𝑎𝑏 from 0 to 0.7
radians in increments of 0.01 radians. Each iteration was run for 25 seconds
and the angle of rotation, 𝜃 was recorded and plotted (Figure 39). The
parameters in Table 17 were kept constant. It can be seen that the
relationship between the rate of angular rotation and amplitude of the rotor
rotation is non-linear.
Parameter
Value
𝑎𝑟
𝜔𝑓
𝜔𝑏
𝜔𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.7 rad
1 rad/s
1 rad/s
2 rad/s
0 rad
0 rad
0 rad
rate of rotation platform amplitude test
Figure 39 – Rate of rotation for a range of platform
amplitudes
– 26 –
5.2.3.
Initial Velocity
Rather than starting with an initial velocity of zero, the parking gait was
investigated with an initial velocity. The parameters in Table 18 were used
and an initial velocity of 0.5 m/s was applied. The simulation was run for 60
seconds and the trajectory of the snakeboard is shown in Figure 40.
Parameter
Value
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝜔𝑓
𝜔𝑏
𝜔𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
0.7 rad
1 rad/s
1 rad/s
2 rad/s
0 rad
0 rad
0 rad
initial velocity test
Figure 40 – Trajectory with an initial velocity applied
Then an identical simulation was run except the angular frequencies were
increased so the parameters in Table 19 were used. The trajectory is shown
in Figure 41.
Parameter
Value
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝜔𝑓
𝜔𝑏
𝜔𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
0.7 rad
1.5 rad/s
1.5 rad/s
3 rad/s
0 rad
0 rad
0 rad
initial velocity test
Figure 41 – Trajectory with an initial velocity applied
It can be seen that increasing the angular frequency reduces the radius of
the circular trajectory. Attempts were made to quantify this change but
without success. This is an area for further work because an understanding
of it and the resonant gait would allow full motion planning.
– 27 –
6.
Parking Gait
The “parking” gait is the third and final one discussed in [1]. It occurs when
the angular frequencies 𝜔𝑓 , 𝜔𝑏 and 𝜔𝑟 are at a ratio of 2:2:3. It produces a
net displacement in the 𝑦-direction. Like the rotation gait, the parking gait is
not one used by snakeboard riders.
As an example of the parking gait, Figure 42 shows the trajectory of the
Figure 21. The controlled parameters over time are shown in Figure 43.
Parameter
Value
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝜔𝑓
𝜔𝑏
𝜔𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
0.7 rad
2 rad/s
2 rad/s
3 rad/s
0 rad
0 rad
0 rad
parking gait example
Figure 42 – Trajectory of snakeboard during parking
gait
Figure 43 – Control Parameters during parking gate
6.1.
Direction
Like the other gaits the first priority was to understand how to control the
direction of net displacement in the 𝑦-axis. The four combinations of
amplitude values which produced at net displacement were tested in a
– 28 –
simulation with a run time of 15 seconds (Table 22). The parameters in Table
21 were kept constant.
Parameter
Value
𝜔𝑓
𝜔𝑏
𝜔𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
2 rad/s
2 rad/s
3 rad/s
0 rad
0 rad
0 rad
Table 21 – Constant parameters for parking gait direction test
𝒂𝒇 (rad)
𝒂𝒃 (rad)
0.3
0.3
-0.3
-0.3
-0.3
-0.3
0.3
0.3
𝒂𝒓 (rad) Net displacement in the 𝒚-direction Trajectory
-0.7
0.7
0.7
-0.7
Positive
Positive
Negative
Negative
Figure 44
Figure 45
Figure 46
Figure 47
Table 22 – Results for parking gait direction test
Figure 44
Figure 45
Figure 46
Figure 47
– 29 –
Unlike the rotation and resonance gait there is no simple rule for determining
which direction the net 𝑦-displacement will be in. Also, each of the possible
directions for the parking gait can be offset in the positive or negative 𝑥direction.
6.2.
Rate of change in the 𝒚-direction
The rate of change in the 𝑦-direction can be changed by varying the
amplitudes or angular frequencies of the controlled parameters, and are
investigated in the following sections.
6.2.1.
Angular Frequency
To investigate the effect of angular frequency a script was created that varied
𝜔𝑓 and 𝜔𝑏 from 0 to 2 in increments of 0.1 radians while keeping a ratio of
3𝜔𝑟 = 2 𝜔𝑓 = 2 𝜔𝑓 . Each iteration was run for 25 seconds and the 𝑦displacement was recorded and plotted (Figure 48). The parameters in Table
23 were kept constant. The relationship between rate of change in the 𝑦direction and the angular frequencies is reasonably linear but unstable in the
larger angular frequencies.
Parameter
Value
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
-0.7 rad
0 rad
0 rad
0 rad
angular frequency variation
Figure 48 – Displacement for a range of platform
frequencies
– 30 –
6.2.2.
Amplitude of platforms and rotors
The first amplitude investigation was changing 𝑎𝑟 from 0 to 0.7 radians in
increments of 0.01 radians. Each iteration was run for 25 seconds and the 𝑦displacement was recorded and plotted (Figure 49). The parameters in Table
24 were kept constant. The investigation shows that the relationship between
the rate of angular rotation and amplitude of the rotor rotation is non-linear.
Parameter
Value
𝑎𝑓
𝑎𝑏
𝜔𝑓
𝜔𝑏
𝜔𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
1 rad/s
1 rad/s
2 rad/s
0 rad
0 rad
0 rad
Table 24 – Constant parameters for rotor
amplitude variation
Figure 49 – Displacement for a range of rotor
amplitudes
The second amplitude investigation was changing 𝑎𝑓 and 𝑎𝑏 from 0 to 0.7
radians in increments of 0.01 radians. Each iteration was run for 25 seconds
and the 𝑦-displacement was recorded and plotted (Figure 50). The
parameters in Table 25 were kept constant. The investigation shows that the
relationship between the rate of angular rotation and amplitude of the rotor
rotation is non-linear.
Parameter
Value
𝑎𝑟
𝜔𝑓
𝜔𝑏
𝜔𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.7 rad
1 rad/s
1 rad/s
2 rad/s
0 rad
0 rad
0 rad
Table 25 – Constant parameters for platform
amplitude variation
Figure 50 – Displacement for a range of platform
amplitudes
– 31 –
6.3.
Initial Velocity
Rather than starting with an initial velocity of zero, the parking gait was
investigated with an initial velocity. The parameters in Table 20 were used
and an initial velocity of 0.5 m/s was applied. The simulation was run for 100
seconds and the trajectory of the snakeboard is shown in Figure 51. The
velocity over time is plotted in Figure 52.
Figure 51 – Trajectory of snakeboard when an initial velocity is applied to the parking gait
Figure 52 – Snakeboard velocity when an initial velocity is applied to the parking gate
Once an initial velocity is applied, the trajectory is in a straight line, similar to
resonance gait. Interestingly the average velocity stays relatively constant
over time unlike the resonance gait.
– 32 –
7.
Direct Simulation Controller
Most of the research on snakeboard dynamics has focused on using
mathematical tools to analyse how they behave. Using sinusoids to
represent the periodic motions came from observation but to date no serious
attempt at recording a real snakeboard has been made.
Ideally, the inputs, platform angle and body position would be recorded as a
function of time while simultaneously recording the trajectory of the
snakeboard. From this, an understanding of how a real rider negotiates
obstacles, generates momentum and controls the snakeboard could be
gained. Applying this knowledge back into computer simulations could lead
to better motion planning algorithms and further the field of nonholomonic
robots, like the bio-inspired ones mentioned in the introduction.
For this investigation, various methods of recording the movements of a real
snakeboard were proposed and evaluated. The most accurate method would
be using motion capture technology like that used in the film industry. There
are facilities at the University of Strathclyde to do this, however, it was
decided that it was not suitable for this project. Getting the data recorded into
a form that could be analysed would be a time consuming and difficult task.
In addition, access to the equipment would be limited.
Another option was to attach recording devices to a snakeboard. The
problem with this approach is finding a way to record the path. Accurately
determining the absolute position of the snakeboard is not a trivial task.
The method chosen was to construct a small device with three rotational
inputs (front platform, back platform and body/rotor rotation). This device was
connected to a computer running with a MATLAB simulation that took the
inputs and in real-time used them in the mathematical model of the
snakeboard. A graphical output was shown to user (Figure 14). It was found
that having the camera move relative to the snakeboard in the 𝑥 and 𝑦
direction was beneficial. It was possible to control the simulation when the
camera rotated relative to 𝜃, however, it was distracting so this was not taken
forward.
– 33 –
Another benefit of the construction this device is the possibility for future
work. It is ideally suited to directly controlling a robotic snakeboard and
testing its behaviour. Standard input devices such as joysticks, keyboards
and mice would not be suitable and limited to a method similar to that used in
“Open loop periodic controls”.
Get potentiometer
values and convert to
radians for each
controlled parameter
Get the time period
since timer was
started . Reset the
timer and start it
Update the visual
display of the
snakeboard
Solve the equations
of motion to obtain
the path of the
snakeboard for the
time period
Use the time period,
previous controlled
parameters and the new
ones to calculate velocity
and acceleration of
controlled parameters
Figure 53 – Direct Simulation Controller Flowchart
7.1.
Limitations
It is assumed that the mathematical model is suitably accurate. If there are
errors with the model, the user might unconsciously compensate for them.
However, any obvious errors would be apparent to someone familiar to riding
a real snakeboard so this controller acts as a validation to the mathematical
model.
An obvious difference between this input device and a real snakeboard is
that hands are used instead of standing on a board. It was assumed that this
should not be an insurmountable problem and in practice this proved to be
the case. After some practicing, it became possible for the author to use his
hands to control the virtual snakeboard with reasonable proficiency. It was
observed that other users struggled to produce any kind of motion, just like
– 34 –
beginner snakeboard riders. This is probably because the experience from
riding a real snakeboard translates to controlling a virtual one.
The lack of feedback apart from visual (the animation on screen) is a
limitation because a rider‟s inputs are influenced by the forces felt by their
body moving.
7.2.
Construction
7.2.1.
Components
Table 26 lists the components used to create the controller and their cost.
The overall total was £8.28. Parts were chosen for their low cost and
availability.
Component
Quantity Unit Cost (£) Cost (£)
PICAXE 08M
Linear Potentiometer
Potentiometer Shaft
Stripboard
10k Resistor
22k Resistor
3.5mm Socket
LED
Enclosure
Mass
Wire
Wooden Lollipops
Total Cost
1
3
3
1
1
1
1
1
1
1
4
1.50
0.50
0.80
1.30
0.10
0.10
0.08
0.10
2.00
-
1.50
1.50
1.60
1.30
0.10
0.10
0.08
0.10
2.00
£8.28
Table 26 - Components
7.2.2.
Microcontroller
The purpose of the microcontroller is to convert the analogue signals from
the potentiometers into a digital signal that is transmitted to MATLAB through
a serial connection. The PICAXE 08M microcontroller was chosen because
of its low cost and is easily programmed.
7.2.3.
Final Design
A labelled photograph of the final design is shown in Figure 54.
– 35 –
Serial
connection to Batteries
computer
Mass
Platform
potentiometer
Rotor
potentiometer
Platform
potentiometer
LED
Circuit board
Figure 54 – Labelled photograph of the physical input controller
7.2.4.
Improvements
The potentiometers are not designed for continual use as is required for this
device. Over time, their accuracy will decrease until the controller no longer
functions correctly. It was decided that this was not an issue for this project
because of its limited duration. However, if intensive long term research were
to be carried out then a different method of measuring the angular rotation
would needed such as an LVDT (linear variable differential transformer)
which does not use sliding contacts.
7.3.
Results
Some practice was needed before the author was able to use the controller.
From observing others unfamiliar with the concept of snakeboarding, it is
apparent that being able to ride a real snakeboard is of great benefit when
trying to use this.
Interestingly the author showed the same dominance in his hands as he did
with his feet. In skateboarding terminology, a person is either “regular” or
“goofy” (there is a minority equally adept to both styles just as some
ambidextrous). If your left foot is leading in the direction of travel then you‟re
“regular”. Likewise, if your right foot leads then you‟re “goofy”. Having learnt
– 36 –
how to control a snakeboard with your feet, the skills are probably
“transferred” to your hands.
Accordingly, the simulation was set up to ensure the author caused the
snakeboard to move in the positive 𝑥 direction to enable a simple analysis.
7.3.1.
Validation Test
The first test was to perform a simple validation that the controller and
MATLAB code was working as intended. The front platform was given a
rotational displacement in an anticlockwise direction and the back platform
was given a rotational displacement of similar rotational displacement in a
clockwise direction. The rotor was rotated rapidly in a clockwise direction.
This should cause the snakeboard to accelerate anticlockwise (conservation
of angular momentum). Then the rotor was rotate slowly back to its starting
position which should return the snakeboard to zero velocity.
The controlled parameters as a function of time are shown in Figure 55 and
the velocity is shown in Figure 56.
Figure 55 – Controlled parameters for Validation Test
– 37 –
Figure 56 – Snakeboard velocity during Validation Test
The snakeboard behaved as expected during this test, which is a good
indication the simulation is working correctly.
7.3.2.
Resonance imitation test
The aim of this test is to try to imitate the resonance gait. The results shown
in (Figure 57, Figure 58 and Figure 59) are representative of the typical
attempts made.
Figure 57 – Controlled parameters for Resonance imitation test
– 38 –
Figure 58 – Trajectory of the snakeboard for Resonance imitation test
Figure 59 – Velocity of the snakeboard during Resonance imitation test
The trajectory of the snakeboard is clearly not a replication of the ones
produced by the controlled parameters created using periodic controls. The
oscillations about the 𝑦-axis vary greatly instead of gradually increasing. This
can be expected as the controlled parameters created with a sinusoidal
function can be precisely controlled whereas the controller requires direct
human input. The sensitivity of the snakeboard to the controlled parameters
is explored further in section 9 regarding bifurcation.
From Figure 59 it can be seen that the snakeboard initially accelerates
rapidly before settling to oscillations about 0.8m/s. Attempts to increase the
velocity proved impossible. This suggests that it is relatively easy to maintain
– 39 –
a certain velocity but trying to go faster is significantly more difficult. Trying to
quantify this is difficult because by their nature, planned controlled
parameters are consistent. Future work could try to replicate the inaccuracy
of controlled parameters by applying a degree of randomness.
This would be beneficial because nonholomonic systems in the real world
will always have some imprecision. This could be in the physical components
such as motors or control systems. Using the example of a robotic
snakeboard, if the platforms could not be positioned accurately then its
velocity would be limited.
7.3.3.
Rotation Imitation Test
The aim of this test is to try to imitate the rotation gait. The results shown in
(Figure 60, Figure 61 and Figure 62) are representative of the typical
attempts made.
Figure 60 – Controlled parameters for Rotation Imitation Test
Figure 61 – Trajectory for Rotation Imitation Test
– 40 –
Figure 62 – Velocity of the snakeboard during Rotation Imitation Test
Although rotation is achieved, the trajectory is erratic. Similarly, the controlled
variables are unclear, appearing similar to those used for the resonance gait
except for some variations. This makes sense, as a resonance gait
combined with small alterations is how a human riding a real snakeboard
would control it. The snakeboard maintains a reasonably constant average
speed.
– 41 –
8.
Open loop periodic controls
Whilst experimenting with varying the controlled parameters it was noticed
that trying different combinations to observe the behaviour of the snakeboard
was taking a long time. Each simulation has to have its parameters adjusted
in a text file before being run and the results viewed. This does allow
accuracy and control which is why the majority of the work done in this paper
used this method.
The open loop, periodic controls developed and covered in this section
sacrifices precision for ease of use. The periodic inputs for the rotor and the
platforms can be changed using a GUI (graphic user interface) which
controls all the parameters, shown in Figure 63.
Figure 63 – GUI for “Open loop periodic controls”
While the user changes parameters in the GUI a real-time 3D model is
displayed (Figure 14). This allows the user to observe the behaviour as
changes are made which facilitates experimentation. This method is much
easier to use than the physical controller that requires practice.
An example of when this open loop control was used is for the motion
planning section. After giving the snakeboard an initial velocity, various
changes were made using the GUI to determine how to slow it down and
what affected the rate of deceleration.
– 42 –
The open loop periodic control simulation works by taking the controlled
parameters (determined by the values in the GUI) and solving the equations
of motion for a very small timestep. This is repeated continuously so that it
appears to be in real time.
Another option available to the user was enabling a “crumb trail”. At fixed
intervals, a marker is left at the current position of the centre of mass of the
snakeboard. This proved to be an effective way of tracking the path of the
snakeboard and showing it to the user. An example of the output displayed
to the user when the “crumb trail” is turned on is shown in Figure 64.
Figure 64 – “Crumb trail”
Although this simulation, using open loop periodic controls, was not used for
the research done in this paper, it contributed indirectly. It proved invaluable
as a tool to become familiar with the snakeboard as an abstract system and
bridged the gap of the author‟s intuitive knowledge of snakeboards and nonholomonic systems. It is a good aid to visualising how the controlled
parameters cause motion in the snakeboard and was inspiration for further
work carried out in this paper.
– 43 –
9.
Bifurcation study
A bifurcation is said to occur when a small, gradual change applied to the
input parameters causes an abrupt „qualitative‟ change in the behaviour of
the system. For the case of the snakeboard system, this means a small
alteration of the controlled parameters causes a qualitative change in the
trajectory.
Bifurcation theory was used to analyse the motion of the snakeboard in
resonance gait to investigate its sensitivity and to gain a better understanding
of its motion. The investigation was split between phase shifts and angular
frequency changes.
A MATLAB script was created that automated the process of trying each
parameter combination. The trajectory of the snakeboard was plotted and
saved as an image file to examine later. Each simulation in this section was
run for 100 seconds. This relatively long duration was chosen to ensure the
full behaviour of the snakeboard was captured rather than a small snapshot.
9.1.
Phase shift
9.1.1.
Rotor phase shift
The parameters in Table 27 were kept constant as 𝛽𝑟 was increased from 0
to 2 in increments of 0.01 radians.
Parameter
Value
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝜔𝑓
𝜔𝑏
𝜔𝑟
𝛽𝑓
𝛽𝑏
0.3 rad
-0.3 rad
0.7 rad
1 rad/s
1 rad/s
1 rad/s
0 rad
0 rad
Table 27 – Constant parameters for rotor phase shift
A bifurcation point was found when 𝛽𝑟 = 1.5 radians which corresponds to
the rotor being approximately 90° out of phase with the platforms. The
trajectory is shown in Figure 65. The snakeboard travels a short distance in
– 44 –
the positive 𝑥 and 𝑦 direction before turning and travelling in the negative 𝑥
and 𝑦 direction.
Figure 65 – Bifurcation point at 𝜷𝒓 = 𝟏. 𝟓𝟖 radians
9.1.2.
Platform phase shift
The parameters in Table 28 were kept constant as 𝛽𝑓 was increased from 0
to 2 in increments of 0.01 radians. Another test was performed were the
parameters in Table 29 were kept constant as 𝛽𝑏 was increased from 0 to 2
in increments of 0.01 radians. The purpose of running similar tests for 𝛽𝑓 and
𝛽𝑏 was to investigate whether or not there is a symmetrical property of the
snakeboard where it does not matter if a phase change is made in the front
or back platform.
Parameter
Value
Parameter
Value
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝜔𝑓
𝜔𝑏
𝜔𝑟
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
0.7 rad
1 rad/s
1 rad/s
1 rad/s
0 rad
0 rad
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝜔𝑓
𝜔𝑏
𝜔𝑟
𝛽𝑓
𝛽𝑟
0.3 rad
-0.3 rad
0.7 rad
1 rad/s
1 rad/s
1 rad/s
0 rad
0 rad
Table 28 – Constant parameters for first
platform phase shift test
Table 29 – Constant parameters for second
platform shift test
Although no bifurcation points were found, this test made is clear that it does
matter which platform has the phase change applied to it. For example, a
phase change of 0.69 is applied to the front platform (Figure 66) and the
back platform (Figure 67).
– 45 –
Figure 66 – Trajectory when 𝜷𝒇 = 𝟎. 𝟎𝟔𝟗 rad
Figure 67 – Trajectory when 𝜷𝒃 = 𝟎. 𝟎𝟔𝟗 rad
The platform that is leading is more sensitive to changes than the trailing
platform. When phase changes were applied to the back platform, the
trajectory barely changed. However, phase changes applied to the front
platform caused erratic behaviour and the snakeboard covered only half the
distance.
9.2.
Angular Frequency
9.2.1.
Rotor angular frequency
The parameters in Table 30 were kept constant as 𝜔𝑟 was increased from 0
to 2 in increments of 0.01 rad/s.
Parameter
Value
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝜔𝑓
𝜔𝑏
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
0.7 rad
1 rad/s
1 rad/s
0 rad
0 rad
0 rad
Table 30 – Constant parameters for rotor angular frequency test
– 46 –
It was found that although the trajectories varied greatly when the angular
frequency way changed, there was no specific bifurcation point.
9.2.2.
Platform angular frequency
The parameters in Table 31 were kept constant as 𝜔𝑓 was increased from 0
to 2 in increments of 0.01 rad/s. Another test was performed where the
parameters in Table 32 were kept constant as 𝜔𝑏 was increased from 0 to 2
in increments of 0.01 for the same reasons given in “Platform phase shift”.
Parameter
Value
Parameter
Value
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝜔𝑏
𝜔𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
0.7 rad
1 rad/s
1 rad/s
0 rad
0 rad
0 rad
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝜔𝑓
𝜔𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
0.7 rad
1 rad/s
1 rad/s
0 rad
0 rad
0 rad
Table 31 – Constant parameters for first
platform angular frequency test
Table 32 – Constant parameters for second
platform angular frequency test
A bifurcation point was found for both the front and back platform at an
angular frequency of 0.01 rad/s (Figure 70 and Figure 71). When either the
front or back platform was stationary, the trajectory strongly resembled the
resonance gait (Figure 68 and Figure 69). The implication of this is that
motion can be achieved with only one rotating platform. However, the
platform that is not rotating must stay perfectly still or the trajectory will be
irregular.
Figure 68 – Trajectory when 𝝎𝒇 = 𝟎. 𝟎𝟏 rad/s
– 47 –
Figure 69 – Trajectory when 𝝎𝒃 = 𝟎. 𝟎𝟏 rad/s
Figure 70 – Trajectory when 𝝎𝒇 = 𝟎 rad/s
Figure 71 – Trajectory when 𝝎𝒃 = 𝟎 rad/s
Unlike the situation of front and back platforms being out of phase, there was
no significant difference as to which platform was altered. An example of this
is shown in the figures below. Figure 73 is the trajectory when 𝜔𝑓 = 1.21
rad/s and Figure 78 is the trajectory when 𝜔𝑏 = 1.21 rad/s. While not
identical, they are very similar and this is representative of all the values of
angular frequencies plotted.
Figure 72 – Trajectory when 𝝎𝒇 = 𝟏. 𝟐𝟏 rad/s
Figure 73 – Trajectory when 𝝎𝒃 = 𝟏. 𝟐𝟏 rad/s
– 48 –
10.
Motion Planning
Motion planning for nonholomonic systems is an area receiving a lot of
academic attention. For this project, it was decided a basic motion planner
should be created. The goal was to create an algorithm that would output
controlled parameters that could enable the snakeboard to travel a path with
a specified displacement in the 𝑥-axis in a given time. The resonance gait is
used give a one-dimensional displacement and the snakeboard should start
and end at rest.
Since a resonance gait is used the angular frequencies for the platforms and
rotors are assumed to equal (𝜔 = 𝜔𝑓 = 𝜔𝑏 = 𝜔𝑟 ) for motion planning.
Two preliminary tests (described in “Speeding up and slowing down” and
“Parameter to be changed”) were carried out to gain a better understanding
of the snakeboard‟s behaviour in the process used for motion planning.
10.1.
Speeding up and slowing down
To prepare for motion planning tests were performed where the snakeboard
was accelerated and then decelerated. These investigated how various
distances could be travelled at different speeds and bring the snakeboard
back to rest.
10.1.1. Test 1
The first test used the parameters in Table 33 for 30 seconds before
changing to the parameters in Table 34 for the final 30 seconds. The
trajectory is plotted in Figure 74 and the velocity against time in Figure 75.
Parameter
Value
Parameter
Value
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝜔
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
0.7 rad
2 rad/s
0 rad
0 rad
0 rad
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝜔
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
-0.7 rad
2 rad/s
0 rad
0 rad
0 rad
Table 33 – Parameters for initial period
Table 34 – Parameters for final period
– 49 –
Figure 74 – Trajectory during Test 1
Figure 75 – Velocity during Test 1
10.1.2. Test 2
The second test used the same parameters as Test 1 for the initial and final
period (Table 33 and Table 34) but for 60 seconds each period. The
trajectory is plotted in Figure 76 and the velocity against time in Figure 77
Figure 76 – Trajectory during Test 2
Figure 77 – Velocity during Test 2
– 50 –
The final velocity is 0.0135 m/s. This is further away from the ideal zero
velocity than the previous test. From this, it was concluded that the longer
the simulation was run for, the further the final velocity would be from zero.
However, the final velocity is very low and could be considered negligible
and is probably due to small errors in the simulation. For motion planning,
either the time for deceleration could be increased so the snakeboard comes
to completely rest or allow for a small error.
In theory the final position of the snakeboard should have the same
displacement in the 𝑦-axis as the initial position because of the symmetrical
path created from accelerating and decelerating for the same length of time.
Due to errors it is possible the displacement in the 𝑦-direction might not be
exactly the same. However, the difference will be negligible compared to the
displacement in the 𝑥-direction so it will be ignored.
It was decided that in order to simplify the problem a tolerance of +/- 0.1 m/s
would be applied.
10.2.
Parameter to be changed
From the resonant section there are three parameters that can be adjusted
that would be suitable for varying the time taken in the motion planning
algorithm by changing the acceleration of the snakeboard: rotor amplitude,
platform amplitudes and the angular velocities.
To test these parameters in the context of motion planning a test was
performed for each parameter. For each increment the time taken to get to
0.5 m/s from rest and the distance travelled in the 𝑥-direction was recorded.
The results from the tests in the previous section showed that if the same
parameters were applied for the same length of time (except the rotor
amplitude that was opposite) the snakeboard would come to rest.
10.2.1. Rotor Amplitude Test
The parameters in Table 35 were kept constant as the rotor amplitude,
𝑎𝑟 , was increased from 0.2 radians to 1.5 radians in increments of 0.1
radians. The results are shown in Figure 78.
– 51 –
Parameter
Value
𝑎𝑓
𝑎𝑏
𝜔
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
1 rad/s
0 rad
0 rad
0 rad
Table 35 – Constant parameters for rotor amplitude test
Figure 78 – Time taken to reach 0.5 m/s for a range of rotor amplitudes
From Figure 78 it can be seen that there is an exponential relationship for the
time taken and distance travelled as the rotor amplitude is increased. In this
configuration an extremely large rotor amplitude would be needed to get to a
velocity of 0.5 m/s in a time significantly less than 50 seconds. Likewise, an
extremely large rotor amplitude would be needed to get to 0.5 m/s in a
distance less than 10m.
10.2.2. Platform Amplitudes Test
The parameters in Table 36 were kept constant as the platform amplitudes
were increased from 0.2 to 1 radian. The results are shown in Figure 79.
Parameter
Value
𝑎𝑟
𝜔
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.7 rad
1 rad/s
0 rad
0 rad
0 rad
Table 36 – Constant parameters for platform amplitudes test
– 52 –
Figure 79 – Time taken to reach 0.5 m/s for a range of platofrm amplitudes
When the platform amplitude is varied from 0.2 to 0.6 radians, the
relationship is similar to the rotor amplitude test. However, as was discussed
in the resonance section the trajectory becomes unstable much faster when
large platform amplitudes are used.
10.2.3. Velocity Test
The parameters in Table 37 were kept constant as the angular velocities 𝜔
were increased from 0.8 to 2.0 rad/s in increments of 0.1 rad/s. The results
are shown in Figure 80.
Parameter
Value
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
-0.3 rad
0.7 rad
0 rad
0 rad
0 rad
Table 37 – Constant parameters for velocity test
– 53 –
Figure 80 – Time taken to reach 0.5 m/s for a range of angular frequencies
Figure 80 shows the time taken to reach 0.5 m/s. The distance travelled in
the 𝑥-direction decreases exponentially as the angular velocity of the
platforms and the rotor is increased.
10.2.4. Conclusions
Varying the platform amplitudes can be immediately discounted for the
purposes of this motion planning algorithm because the range they can be
varied is very limited without causing instability.
Whilst varying the rotor amplitude is suitable for slow accelerations, if a fast
accelerating were required an extremely large amplitude would be
necessary. In comparison, changing the angular velocities offers a large
scope for motion planning.
Therefore, it was decided that changing the angular velocity of the platforms
and rotors is the best parameter to change.
– 54 –
10.3.
Algorithms
From the preliminary tests described previously, it was possible to begin
developing a motion planning algorithm. All algorithms developed for this
project are numerical solutions scripted in MATLAB.
It was not originally intended to produce more than one algorithm. However,
when the solutions were taking a long time to generate it was clear
improvements needed to be made and the code optimised. A standard test
was created (Table 38) to allow a comparison of the algorithms using the
fixed control parameters (Table 39).
Requirement
Value
Displacement
30m
Duration
100 seconds
Table 38 – Requirements to test motion
planning algorithm
Parameter
Value
𝑎𝑓
𝑎𝑏
𝑎𝑟
𝛽𝑓
𝛽𝑏
𝛽𝑟
0.3 rad
0.3 rad
-0.7 rad
0 rad
0 rad
0 rad
Table 39 – Fixed parameters
The time taken to produce a result at the given accuracy was measured. It is
acknowledged that computing power available will determine this but since
only a rough comparison was needed it would be suitable providing it was
done on the same computer.
All the algorithms in this paper have the snakeboard accelerating for half of
the simulation time and decelerating for the second half.
– 55 –
10.3.1.
Version 1
The flowchart shown in Figure 81 describes the logic used in this algorithm.
The algorithm begins by calculating the distance travelled at a minimal time
period and ω. If the distance travelled was not sufficient, the period was
increased until it was enough. If the current period was greater than the
duration required, then the snakeboard will need to accelerate and
decelerate faster to travel further. To do this 𝜔 is increased for the reasons
given previously. Eventually an 𝜔 value will be found that allows the
snakeboard to travel the required distance in the duration specified.
Get the xdisplacement,
duration from the
user. The current
period is set to a
minimum and 𝜔 to
0.1
Run the simulation
for the current
period at the current
𝜔 value
Is the calculated
displacement
greater than the
required
displacement?
Increase the
period of the
simulation by
an increment
of time
NO
YES
Increase 𝜔
by 𝜔increment.
Reset current
period to
minimum
NO
Is the current
period the
same as the
required time?
YES
Parameters
successfully
found, output to
user
Figure 81 – Motion Planning (Version 1) Flowchart
– 56 –
Using the standard test, it was found that 𝜔 = 1.62 rad/s for 98 seconds
caused the snakeboard to travel 15.4m. The time taken to reach the solution
was 281 seconds.
It was decided that the calculation time was unacceptably high, indicating the
algorithm was very inefficient. The reason for this is that many time periods
are evaluated for each value of 𝜔, even when that value is not a solution. For
low values of 𝜔 this is a fundamental problem because it can require a very
large period for the snakeboard to travel the required distance.
Another problem with this algorithm is that it cannot begin at 𝜔 = 0 rad/s
because no matter how long the simulation ran for it would not move. This
means 𝜔 must start at an arbitrarily small value. If this happens to be larger
than the solution then the results will be inaccurate.
The accuracy of the solution can be improved by decreasing the time
increment and the 𝜔 increment but this will increase the time taken to reach
a solution.
– 57 –
10.3.2. Version 2
This version of the motion planning algorithm improved significantly on the
previous one because it only evaluates each 𝜔 value once. Since the
duration is specified, the simulation is evaluated for that period.
The flowchart in Figure 82 describes the logic used in this algorithm. It
begins with 𝜔 at zero and steadily increases 𝜔 until the snakeboard travels
far enough. Each simulation is run for the specified duration.
duration from the
user. Set 𝜔 to zero
Run the simulation
for the specified
duration at the
current 𝜔 value
Is the calculated
displacement
greater than the
required
displacement?
NO
Increase 𝜔
by 𝜔increment
YES
Parameters
successfully
found, output to
user
Using the standard test and 𝜔-increment = 0.01, it was found that 𝜔 = 1.57
for 100 seconds caused the snakeboard to travel 15.02m. The time taken to
– 58 –
reach the solution was 4.54 seconds. This algorithm is simpler than the
previous one and offers a significant improvement because the solution time
has been reduced by a significant factor,
The accuracy of the result depends on the 𝜔 increment. A smaller increment
will give increased accuracy at the expense of extra computational time
because more iterations are needed.
The standard test was run again but this time with an 𝜔-increment of 0.001. It
was found that 𝜔 = 1.569 for 100 seconds caused the snakeboard to travel
15.0042m. The distance travelled is now closer to what was required,
however, the computational time increased to 42.78 seconds.
10.3.3. Version 3
The previous version has a compromise between accuracy and speed.
Because it is desirable to obtain an accurate solution in the least amount of
time, a new algorithm was developed.
The flowchart in Figure 83 describes the logic used in this algorithm. It is an
extension of the previous version because it begins large 𝜔 increments until
an estimate of the controlled parameters is found. It then refines 𝜔 with
decreasing 𝜔-increments until a solution of sufficient accuracy is obtained.
During testing, it was sometimes found that the solution would not converge
to the accuracy required. This is probably due to small errors in the
mathematical model and the non-linear nature of the system. When this
happened, 𝜔-increment decreased exponentially towards zero. To prevent
this from happening a minimum 𝜔-increment was defined and the algorithm
aborted when this was reached.
– 59 –
duration and
accuracy from the
user. Set 𝜔 to zero
Run the simulation
for the specified
duration at the
current 𝜔 value
Is the calculated
displacement
greater than the
required
displacement?
NO
Increase 𝜔
by 𝜔increment
YES
Parameters
successfully
found, output to
user
NO
Go back to the
previous 𝜔
value and
reduce the 𝜔increment size
Did it
overshoot
the desired
accuracy?
YES
Solution is not
converging
YES
Is the 𝜔increment
too small?
NO
Using the standard test, it was found that 𝜔 = 1.556035041809082 rad/s for
caused the snakeboard to travel 30.000004 m. The time taken to reach the
solution was 2.63 seconds. The level of accuracy for 𝜔 needs to be
extremely high to travel precise displacements.
Again, the solution time has been reduced compared to the previous
algorithms. Figure 85 shows how the iterative process efficiently converges
upon the solution. As intended, the, 𝜔-increment decreases as the algorithm
converges on the solution.
– 60 –
Figure 84 – Iterative process
10.4.
Figure 85 – Output to user
Improvements
Only straight line motion planning is implemented in this algorithm. Although
this is useful, full motion planning would require ability to orientate the board.
This could be achieved by continuing the work done in the section titled
“Rotation Gait”.
The solutions generated by this motion planning algorithm have the
snakeboard accelerate and decelerate for the same length of time. A better
algorithm would have the snakeboard accelerate rapidly at the start with
large omega and amplitudes before settling down to a constant velocity in a
way similar to that of the results of the controller device.
The stable resonant gait method could be employed to limit the amplitude of
the oscillations about the 𝑥-axis. This would allow greater distances and
eliminate the problems associated with instability.
This algorithm does not take into account the physical limitations of a real
world implementation, a heavy rotor can only accelerate so quickly. Ideally,
the algorithm would compromise between different adjustments to the control
parameters instead of only optimising 𝜔. For instance when the maximum
amplitude of rotation for the platforms was reached the frequency would
increase.
– 61 –
11.
Stable Resonance Gait
As mentioned previously, amplitudes in the 𝑦-direction can become large
enough to cause the board to turn around in the resonant gait. This is
particularly true when long simulation run times are used with large platform
amplitudes.
The purpose of the work in this section is create a script that covers the
greatest distance in the 𝑥-distance in a given amount of time. It should be
capable of long run times that are normally problematic when using the
resonance gait.
For the initial algorithm, the parameters that could change over time were 𝑎𝑓 ,
𝑎𝑏 and 𝑎𝑟 . The platform angles were assumed equal and opposite (𝑎𝑝 = 𝑎𝑓 =
−𝑎𝑏 ). Maximum and minimum values for 𝑎𝑝 and 𝑎𝑟 were assigned as
parameters that could be controlled by the user.
Because the resonance gait was used, the controlled parameters had the
same frequency. As described in Figure 86, the controlled parameters can
be split into “cycles”. Like wavelengths, their period depends on the
frequency 𝜔 (for the purposes of this section, 𝜔 = 𝜔𝑓 = 𝜔𝑏 = 𝜔𝑟 ).
Cycle 1
Cycle 2
Figure 86 – “Cycles” in the controlled parameters for
resonant gait
The algorithm works by evaluating every possible combination of amplitudes
and determining which arrangement produces the greatest displacement in
– 62 –
the 𝑥-direction for that cycle. The best combination of amplitudes is then run
and the end conditions for that cycle are used for the beginning of the next
cycle. This continues until the total duration of all the cycles is greater or
equal to the given time.
The accuracy number of combinations depended on the resolution in the
range of 𝑎𝑝 and 𝑎𝑟 that were used. A small resolution produces results that
are more accurate but takes longer to compute.
The algorithm was tested using a range for 𝑎𝑝 from 0 to 0.7 in increments of
0.001 radians and a range of 𝑎𝑟 from 0 to 0.7 in increments of 0.001 radians.
The angular frequency 𝜔 was fixed at 1 rad/s and the run time was 12𝜋
seconds.
Figure 87 – Trajectory during stable resonance gait test
Figure 88 – Controlled parameters during stable resonance gait test
– 63 –
Figure 89 – Platform and rotor amplitudes during stable resonance gait test
Figure 90 – Snakeboard velocity during stable resonance gait
The trajectory of the snakeboard (Figure 87) is not perpendicular to the 𝑥axis. It was decided that this was not a significant problem because an initial,
small correction could be applied and the snakeboard‟s trajectory would be
“straight”.
The controlled parameters and 𝑎𝑝 and 𝑎𝑟 as functions of time are plotted in
Figure 88 and Figure 89 respectively. When the snakeboard is initially at
rest, the optimum platform amplitudes is 0.5 radians. This is similar to the
amplitude that what was found in the section regarding the amplitude of the
platforms in resonance gait. After a relatively optimum large platform angle in
the first cycle the later cycles are much smaller at 0.02 and 0.029.
This closely matches the author‟s experience in riding snakeboards and
observing others. The best way to gain momentum is to begin with a large
– 64 –
rotation of the body (rotor) combined with large platform rotations. This gets
the snakeboard moving before smaller oscillations are used.
An initial spike in velocity (Figure 90) occurs before settling down.
This simulation confirms that a big, initial “swing” is the optimum way of
getting speed before settling down. A maximum rotor amplitude is always
best for big 𝑥-displacement. This knowledge could be of use when designing
robots that produce motion in a similar way to snakeboards.
– 65 –
12.
Conclusions
12.1.
Summary
By developing a mathematical model in MATLAB using the equations of
motion for a snakeboard the author has been able to demonstrate some of it
characteristics. The three basic gaits have been analysed to show how the
direction of travel can be controlled. For each gait, the controlled parameters
have been investigated to show how the behaviour can be altered.
Using the knowledge gained from the basic gaits a motion planning algorithm
was developed. It was found that accurate displacements required extremely
precise control parameters that would be almost impossible for robots in the
real world to replicate. To achieve reasonable accuracy a closed loop control
mechanism would need to be implemented.
Research in the bifurcation section showed that generally, the snakeboard
does not exhibit many bifurcation points, instead, small parameter changes
usually lead to small changes in the trajectory.
An algorithm was created to resolve a problem highlighted in the resonance
section that the trajectory became stable over time. This algorithm replicated
the behaviour of a human rider on a snakeboard.
Finally two methods were created to allow a user to control the simulation:
open loop controls and a physical controller. The physical controller proved
to be an interesting way of interacting with the simulation and was useful for
testing different ideas.
– 66 –
12.2.
Evaluation
All the work done in this paper was based on a single MATLAB model. It
would have been beneficial for real snakeboard behaviour to have been
analysed. This would have given additional validation to the results.
Efforts were made to ensure the code written was versatile and could be
adapted for different aspects of the analysis. For this paper, it was soon
realised a set of basic scripts should be created to plot the trajectory,
controlled parameters and velocity for each simulation. This worked well
because it ensured consistency throughout.
For the author to continue this investigation a better understanding of the
mathematics behind nonholomonic systems would be beneficial to enable a
better understanding of the academic papers available.
12.3.
Further Work
To the best of the author‟s knowledge, nobody has yet to use motion capture
technology on a human skateboarder. This could yield useful results for the
field of robotics from analysing the way a human controls the board.
Building a robotic snakeboard would be the next step to evaluating the
motion planning algorithm developed in this paper. In addition, the algorithm
could be extended beyond the linear point developed in this paper. This
could be achieved using the rotation gait combined with the resonance gait.
Finally, the stable resonance gait could be combined with the motion
planning gait which would improve the latter greatly. The stable resonance
gait provides a means for the snakeboard to achieve a reasonable speed
quickly, rather than a slow acceleration.
– 67 –
13.
References
1. Nonholomonic mechanics and locomotion: the Snakeboard example.
Lewis, Andrew D., et al. s.l. : IEEE International Conference on Robotics
and Automation, 1994.
2. Nonholomonic Motion Planning: Steering Using Sinusoids. Murray,
Richard. M. 5, s.l. : IEEE Transactions on Automatic Control, 1993, Vol. 38.
3. Minimum Control-Switch Motions for the Snakeboard: A Case Study in
Kinematically Controllable Underactuated Systems. Ianniti, Stefano and
Kevin, Lynch M. 4, s.l. : IEEE Transactions on Robotics, 2004, Vol. 20.
4. Kinematic controllability and motion planning for the snakeboard. Bullo, F
and Lewis, A D. s.l. : IEEE Transaction on Robotics, 2003, Vol. 19.
5. Some New Robust Pseudo Forward and Rotation Gaits for the
Snakeboard. Asnafi, A. and Mahzoon, M. 5, s.l. : Scientia Iranica, Vol. 15.
6. Veltman, Eddy. Design and realization of an experimental snakeboard.
s.l. : University of Twente, 2004.
7. Further Development of the Mathematical Model of a Snakeboard.
Kuleshow, A. S. Moscow : s.n., 2007.
– 68 –
Appendix I – Timestep
Appendix II – MATLAB Code
1. Differential Equation Solver
%
%
%
%
%
%
Title: Snakeboard Differential Equation Solver
Filename: snakeboard_differential_equations.m
Author: Jonathan Jamieson
Date: September 2011 – April 2012
Purpose: This function solves the equations of motion that govern
a snakeboard's motion over a specified timespan
function
snakeboard=snakeboard_differential_equations(t,x,m,l,J,Jr,Jp,a1,a2,ar,omega_1,omega_2
,omega_r,Bf,Bb,Br)
k_squared = ((J+Jr+2*Jp)/(m*l^2));
d1 = (Jr/m*l);
d2 = (Jp/m*l);
psi_1 = (a1*sin(omega_1*t+Bf)-a2*sin(omega_2*t+Bb));
psi_1_dot = (omega_1*a1*cos(omega_1*t)-omega_2*a2*cos(omega_2*t+Bb));
psi_1_2dot = (((omega_1^2)*omega_1*sin(omega_1*t))+((omega_2^2)*omega_2*sin(omega_2*t+Bb)));
psi_2 = (a1*sin(omega_1*t+Bf)+a2*sin(omega_2*t+Bb));
psi_2_dot =(omega_1*a1*cos(omega_1*t+Bf)+omega_2*a2*cos(omega_2*t+Bb));
psi_2_2dot = ((-(omega_1^2)*omega_1*sin(omega_1*t+Bf))((omega_2^2)*omega_2*sin(omega_2*t+Bb)));
delta = (ar*sin(omega_r*t+Br));
delta_dot = (omega_r*ar*cos(omega_r*t+Br));
delta_2dot = (-((omega_r)^2)*ar*sin(omega_r*t+Br));
snakeboard=[((x(4)*cos(x(3)))(x(4)*(sin((psi_2)))*sin(x(3)))/(cos((psi_1))+cos((psi_2))));
((x(4)*sin(x(3)))+(x(4)*(sin((psi_2)))*cos(x(3)))/(cos((psi_1))+cos((psi_2))));
((x(4)*sin((psi_1)))/(l*(cos((psi_1))+cos((psi_2)))))
(((((d1*((delta_2dot))+d2*((psi_2_2dot)))*sin((psi_1)))/(cos((psi_1))+cos((psi_2))))(x(4)*((((psi_2_dot)*sin((psi_2))*cos((psi_2))+k_squared*(psi_1_dot)*sin((psi_1))*cos
((psi_1)))/(cos((psi_1))+cos((psi_2)))^2)+((((psi_1_dot)*sin((psi_1)))+((psi_2_dot)*s
in((psi_2))))/((cos((psi_1))+cos((psi_2)))^3))*(((sin((psi_2)))^2)+(k_squared*(sin((p
si_1)))^2)))))/(1+((((psi_2))^2)+(k_squared)*((psi_1))^2)/((cos((psi_1))+cos((psi_2))
)^2)))];
2. General Snakeboard Motion
%
%
%
%
%
Title: General Snakeboard Motion
Filename: general.m
Purpose: Change parameters to experiment with various gaits
%% Clear Previous figures, variables etc
clc;
clear all;
close all;
%%
a1
a2
ar
Control Parameters
= +0.3;
= -0.3;
= 0.7;
omega_1 = 1;
omega_2 = 1;
omega_r = 1;
Bf = 0;
Bb = 0;
Br = 0;
%% Define initial conditions and Timespan of Calculation
x0=[0;0;0;0]; % Define initial conditions (x position, y position, theta, velocity);
initial_time = 0; % Starting time
final_time = 500; % Finishing time
timestep = 0.1; % The resolution of the differentiation
%% Define snakeboard properties
m = 75; % kg, mass of snakeboard and rider
l = 0.285; % m, half the length of the snakeboard
J = 0.22; % moment of inertia of crossbar and platforms
Jr = 14; % moment of inertia of rotor
Jp = 0.013; % moment of inertia of platforms
%% Simulation Properties
tspan=initial_time:timestep:final_time; %Define timespan (period to integrate)
%% Solve coupled differential equations (equations of motion of the system)
[t,x]=ode45(@snakeboard_differential_equations,tspan,x0,[],m,l,J,Jr,Jp,a1,a2,ar,omega
_1,omega_2,omega_r,Bf,Bb,Br);
%% Calculate angles over time span
phi_f = a1*sin(omega_1*tspan+Bf); % Front Platorm Angle
phi_b = a2*sin(omega_2*tspan+Bb); % Back Platform Angle
delta = ar*sin(omega_r*tspan+Br); % Rotor Angle
%% Extract values from solver
xpos = x(:,1);
ypos = x(:,2);
theta = x(:,3);
v= x(:,4);
%% Write state and position of the snakeboard as a function of time to a matrix
snakeboard = zeros(numel(tspan),8);
snakeboard(:,1)
snakeboard(:,2)
snakeboard(:,3)
snakeboard(:,4)
snakeboard(:,5)
snakeboard(:,6)
snakeboard(:,7)
snakeboard(:,8)
=
=
=
=
=
=
=
=
tspan;
xpos;
ypos;
theta;
v;
phi_f;
phi_b;
delta;
3. Time Step Test
%
%
%
%
%
Title: Time Step Test
Filename: time_step_test.m
Purpose: Used to test the behaviour of the ODE45 solver
clc;
clear all;
close all;
a1 = +0.3;
a2 = -0.3;
ar = 0.3;
omega_1 = 1;
omega_2 = 1;
omega_r = 1;
Bf = 0;
Bb = 0;
Br = 0;
timestep = 5; % The resolution of the differentiation5
J = 0.22; % kg.m^2, moment of inertia of crossbar and platforms
scrsz = get(0,'ScreenSize');
tic;
ode_time = toc;
%% Control Variables over time
delta_control = (ar*sin(omega_r*t+Br));
%% Create Plots
f = figure('Position',[scrsz(1)+scrsz(3)/4 scrsz(2)+scrsz(4)/6 scrsz(3)/2
scrsz(4)/1.5]);
name = ['Trajectory when time step = ',num2str(timestep)];
plot(x(:,1),x(:,2)),xlabel('x (m)'),ylabel('y (m)'),title(name);
xpos = x(:,1);
final_x = xpos(end)
4. Define Surface Plot
%
%
%
%
%
%
Title: Define Surface Plot
Filename: surface.m
Purpose: Calls the surface plot function and gives it the input
parameters
clc;
clear all;
close all;
%% Control Parameters
a1 = 0.3;
a2 = -0.3;
ar = 0.7;
omega_1 = 1;
omega_2 = 1;
omega_r = 1;
Bf = 0;
Bb = 0;
Br = 0;
m = 75; % mass of snakeboard and rider
l = 0.285; % half the length of the snakeboard
X = 0:0.05:2;
Y = 0:0.05:2;
Z=surface_function(X,Y,m,l,J,Jr,Jp,a1,a2,ar,omega_1,omega_2,omega_r,Bf,Bb,Br,tspan,x0
); % Calculate displacement for each combination
surf(X,Y,Z), shading interp,xlabel('X Label'),ylabel('Y Label'),zlabel('Displacement
in the x-direction (m)'); % Draw surface plot
5. Surface Plot Function
%
%
%
%
%
%
Title: Surface Plot Function
Filename: surface_function.m
Purpose: This function creates a matrix (z) that consists of how
far the snakeboard has gone in the x-direction
function z =
surface_function(X,Y,m,l,J,Jr,Jp,a1,a2,ar,omega_1,omega_2,omega_r,Bf,Bb,Br,tspan,x0);
z = zeros(numel(Y),numel(X)); % Predefine size of matrix
for i_x = 1:numel(X)
for i_y = 1:numel(Y)
% Can be angular frequencies, amplitude, or phase shift
omega_1 = X(i_x);
omega_2 = X(i_x);
omega_r = Y(i_y);
xpos = x(:,1);
x_distance = xpos(end);
z(i_y,i_x) = x_distance;
end
end
6. Parameter variation & rate of change tests
%
%
%
%
%
Title: Parameter varitation & rate of change tests
Filename: parameter_variation.m
Purpose: Defines inputs for the function and plots the results
clc;
clear all;
close all;
%%
a1
a2
ar
Control Parameters
= 0.3;
= -0.3;
= 0.7;
omega_1 = 1;
omega_2 = 1;
omega_r = 1;
Bf = 0;
Bb = 0;
Br = 0;
X = 0:0.01:0.7;
tspan=initial_time:timestep:final_time; % Define timespan (period to integrate)
Z=calculate_measured_parameter(X,m,l,J,Jr,Jp,a1,a2,ar,omega_1,omega_2,omega_r,Bf,Bb,B
r,tspan,x0);
plot(Z(:,1),Z(:,2)),xlabel('X Label'),ylabel('Y Label ');
7. Calculate Measured Parameter
%
%
%
%
%
Title: Calculated measured parameter
Filename: calculate_measured_parameter.m
Purpose: Calculates results for each combination of the parameters
function z =
calculate_measured_parameter(X,m,l,J,Jr,Jp,a1,a2,ar,omega_1,omega_2,omega_r,Bf,Bb,Br,
tspan,x0);
z = zeros(numel(X),2); % Predefine size of matrix
for i_x = 1:numel(X)
a1 = X(i_x);
a2 = -X(i_x);
xpos = x(:,1);
z(i_x,2) = x_distance;
z(i_x,1) = a1;
end
8. Physical simulation controller
%
%
%
%
%
%
Title: Physical simulation controller
Filename: controller.m
Purpose: This function solves the equations of motion that govern
a snakeboard's motion over a specified timespan
clc;
clear all;
close all;
% Get Screen size
% Serial Settings
s = serial ('COM3');
s.BaudRate = 4800;
s.Terminator = 'cr';
fopen (s);
figure;
axis_size = 100;
grid_lines = 1;
ax = axes('XLim',[-axis_size axis_size],'YLim',[-axis_size axis_size],'ZLim',[-1.5
1.5]);
view(2);
grid on;
[Xg,Yg] = meshgrid(-axis_size:grid_lines:axis_size, -axis_size:grid_lines:axis_size);
Zg =.00001*Xg+.0001*Yg;
surf(Xg,Yg,Zg)
colormap white
axis equal
grid on;
set(gcf,'Color','white')
%% Define snakeboard graphical output
%Everything is relative to half the length of snakeboard (l)
% Crossbar
[xb yb zb]=cylinder([l l]);
g(1) = surface(1*xb,0.07*yb,0.07*zb+0.06,'FaceColor','black');
tb = hgtransform('Parent',ax);
set(g,'Parent',tb)
% Rotor
[xr yr zr]=cylinder([l l]);
h(1) = surface(0.5*xr,0.05*yr,0.07*zr+0.08,'FaceColor','green');
tr = hgtransform('Parent',ax);
set(h,'Parent',tr)
% Front Platform
[xp1 yp1 zp1]=cylinder([l l]);
i(1) = surface(0.07*xp1,0.5*yp1,0.07*zp1,'FaceColor','yellow');
tp1 = hgtransform('Parent',ax);
set(i,'Parent',tp1)
% Back Platform
j(1) = surface(0.07*xp2,0.5*yp2,0.07*zp2,'FaceColor','blue');
set(j,'Parent',tp2)
set(gcf,'Renderer','opengl')
drawnow
psi_1_old = 0;
psi_2_old = 0;
delta_old = 0;
initial_time = 0;
tic;
snakeboard = zeros(2000,17);
for t_run=1:1:2000,
pot_values_raw = fscanf(s);
pot_values_raw = fscanf(s);
pot_values = str2num(pot_values_raw);
phi_f = (0*pi/180)+((40*pi/180-0*pi/180)/(776-548))*(pot_values(1)-548);
phi_b = (0*pi/180)+((40*pi/180-0*pi/180)/(785-557))*(pot_values(2)-557);
delta = (0*pi/180)+((60*pi/180-0*pi/180)/(570-420))*(pot_values(3)-420);
if phi_f > 80*pi/180
phi_f = 80*pi/180;
elseif phi_f < -80*pi/180
phi_f = -80*pi/180;
end
if phi_b > 80*pi/180
phi_b = 80*pi/180;
elseif phi_b < -80*pi/180
phi_b = -80*pi/180;
end
if delta > 90*pi/180
delta = 90*pi/180;
elseif delta < -90*pi/180
delta = -90*pi/180;
end
time_change = toc;
final_time = initial_time+time_change;
psi_1 = phi_f - phi_b;
psi_2 = phi_f + phi_b;
psi_1_dot = (psi_1-psi_1_old)/time_change;
psi_1_2dot = psi_1_dot/time_change;
psi_2_dot = (psi_2-psi_2_old)/time_change;
psi_2_2dot = psi_2_dot/time_change;
delta_dot = (delta-delta_old)/time_change;
delta_2dot = delta_dot/time_change;
tic
tspan=initial_time:time_change/2:final_time;
initial_time = final_time;
psi_1_old = psi_1;
psi_2_old = psi_2;
delta_old = delta;
[t,x]=ode45(@snakeboard_differential_equations_controller,tspan,x0,[],m,l,J,Jr,Jp,psi
_1,psi_2,delta,psi_1_dot,psi_2_dot,delta_dot,psi_1_2dot,psi_2_2dot,delta_2dot);
xpos = x(:,1);
ypos = x(:,2);
theta = x(:,3);
velocity = x(:,4);
snakeboard(t_run,:) = [initial_time xpos(end) ypos(end) theta(end) velocity(end)
phi_f phi_b delta psi_1 psi_2 delta psi_1_dot psi_2_dot delta_dot psi_1_2dot
psi_2_2dot delta_2dot];
%% Animate snakeboard
% Draw crossbar
trans = makehgtform('translate',[xpos(end) ypos(end) 0]);
rotz = makehgtform ('zrotate',theta(end));
set(tb,'Matrix',trans*rotz);
% Draw Rotor
trans = makehgtform('translate',[xpos(end) ypos(end) 0]);
rotz = makehgtform ('zrotate',(theta(end)+delta));
set(tr,'Matrix',trans*rotz);
% Draw Front Platform
trans = makehgtform('translate',[(xpos(end)+l*cos(theta(end)))
(ypos(end)+l*sin(theta(end))) 0]);
rotz = makehgtform ('zrotate',(theta(end)+phi_f));
set(tp1,'Matrix',trans*rotz);
% Draw Back Platfrom
trans = makehgtform('translate',[(xpos(end)-l*cos(theta(end))) (ypos(end)l*sin(theta(end))) 0]);
rotz = makehgtform ('zrotate',(theta(end)+phi_b));
% Camera Position
camzoom(1);
camtarget([xpos(end),ypos(end),0]);
campos([xpos(end)-0,ypos(end)+7,15]);
drawnow
%% Obtain results from previous differentiation and set that as the new starting
value
x0 = x(end,:);
x0 = x0.';
end
datetime=datestr(now);
datetime=strrep(datetime,':','_'); %Replace colon with underscore
datetime=strrep(datetime,'-','_');%Replace minus sign with underscore
datetime=strrep(datetime,' ','_');%Replace space with underscore
save(datetime, 'snakeboard');
fclose (s)
delete (s)
9. Differential equation solver for physical simulation
controller
%
%
%
%
%
%
Title: Differential equation solver for physical simulation controller
Filename:snakeboard_differential_equations_controller.m
Purpose: Solves the equations of motion for the simulation
controller
function snakeboard=snakeboard_differential_equations_controller
(t,x,m,l,J,Jr,Jp,psi_1,psi_2,delta,psi_1_dot,psi_2_dot,delta_dot,psi_1_2dot,psi_2_2do
t,delta_2dot)
k_squared = ((J+Jr+2*Jp)/(m*l^2));
d1 = (Jr/m*l);
d2 = (Jp/m*l);
snakeboard=[((x(4)*cos(x(3)))(x(4)*(sin((psi_2)))*sin(x(3)))/(cos((psi_1))+cos((psi_2))));
((x(4)*sin(x(3)))+(x(4)*(sin((psi_2)))*cos(x(3)))/(cos((psi_1))+cos((psi_2))));
((x(4)*sin((psi_1)))/(l*(cos((psi_1))+cos((psi_2)))))
(((((d1*((delta_2dot))+d2*((psi_2_2dot)))*sin((psi_1)))/(cos((psi_1))+cos((psi_2))))(x(4)*((((psi_2_dot)*sin((psi_2))*cos((psi_2))+k_squared*(psi_1_dot)*sin((psi_1))*cos
((psi_1)))/(cos((psi_1))+cos((psi_2)))^2)+((((psi_1_dot)*sin((psi_1)))+((psi_2_dot)*s
in((psi_2))))/((cos((psi_1))+cos((psi_2)))^3))*(((sin((psi_2)))^2)+(k_squared*(sin((p
si_1)))^2)))))/(1+((((psi_2))^2)+(k_squared)*((psi_1))^2)/((cos((psi_1))+cos((psi_2))
)^2)))];
10.
%
%
%
%
%
Open loop periodic controls
Title: Open loop periodic controls
Filename:open_loop.m
Purpose: Open loop control code
clc;
clear all;
close all;
%% Open GUI
fig = openfig('gui_1.fig','reuse');
% reuse or new
x=[0 0 0 0]; % Define initial conditions (x position, y position, theta, velocity);
time_interval = 0.75; % Period of time to integrate over
%Animation Properties
axis_size = 100; % How large to make the output grid
grid_lines = 0.5; % Distance between grid lines
crumb_frequency = 5; % How often "crumbs" are dropped. The smaller the number the
more often they are drooped. Must be an integer.
tspan = -timestep; % This ensures the simulation starts at 0 seconds
animation_pause = 0.01; % Increase or reduce as necessary to give real time result
%% Create animation output window
crumb_counter = 0; % Crumbs can be dropped every so often so the path of the
snakeboard can be observed
ax = axes('XLim',[-axis_size axis_size],'YLim',[-axis_size axis_size],'ZLim',[-1.5
1.5]);
view(2);
grid on;
[Xg,Yg] = meshgrid(-axis_size:grid_lines:axis_size, -axis_size:grid_lines:axis_size);
Zg =.00001*Xg+.0001*Yg;
surf(Xg,Yg,Zg)
colormap white
axis equal
grid on;
set(gcf,'Color','white')
set(gcf,'Position',[scrsz(1)+scrsz(3)/8 scrsz(2)+scrsz(4)/8 scrsz(3)*3/4
scrsz(4)*3/4]); % This sets the size and the position of the animation output window
%% Define snakeboard graphical output
%Everything is relative to half the length of snakeboard (l)
% Crossbar
[xb yb zb]=cylinder([l l]);
g(1) = surface(1*xb,0.07*yb,0.07*zb+0.06,'FaceColor','black');
tb = hgtransform('Parent',ax);
set(g,'Parent',tb)
% Rotor
[xr yr zr]=cylinder([l l]);
h(1) = surface(0.5*xr,0.05*yr,0.07*zr+0.08,'FaceColor','green');
tr = hgtransform('Parent',ax);
set(h,'Parent',tr)
% Front Platform
i(1) = surface(0.07*xp1,0.5*yp1,0.07*zp1,'FaceColor','yellow');
set(i,'Parent',tp1)
% Back Platform
j(1) = surface(0.07*xp2,0.5*yp2,0.07*zp2,'FaceColor','blue');
set(j,'Parent',tp2)
drawnow
while(2 > 1)
tspan= tspan(end)+timestep:timestep:tspan(end)+time_interval; %Define timespan
(period to integrate)
%% Obtain results from previous differentiation and set that as the new starting
value
x0 = x(end,:);
x0 = x0.';
%% Get GUI data
slider_input = gui_1;
slider_input = guidata(slider_input);
a1 = get(slider_input.a1,'Value');
a2 = get(slider_input.a2,'Value');
ar = get(slider_input.ar,'Value');
omega_1 = get(slider_input.omega1,'Value');
omega_2 = get(slider_input.omega2,'Value');
omega_r = get(slider_input.omegar,'Value');
Bf = get(slider_input.Bf,'Value');
Bb = get(slider_input.Bb,'Value');
Br = get(slider_input.Br,'Value');
%% Extract time series data for x position, y position, angle and velocity of
snakeboard
xpos = x(:,1);
ypos = x(:,2);
theta = x(:,3);
velocity = x(:,4);
[xc yc zc]=cylinder([.01 .01]);
%% Animate snakeboard
for i = 1:numel(xpos)
% Draw crossbar
trans = makehgtform('translate',[xpos(i) ypos(i) 0]);
rotz = makehgtform ('zrotate',theta(i));
set(tb,'Matrix',trans*rotz);
% Draw Rotor
trans = makehgtform('translate',[xpos(i) ypos(i) 0]);
rotz = makehgtform ('zrotate',(theta(i))+delta(i));
set(tr,'Matrix',trans*rotz);
% Draw Front Platform
trans = makehgtform('translate',[(xpos(i)+l*cos(theta(i)))
(ypos(i)+l*sin(theta(i))) 0]);
rotz = makehgtform ('zrotate',(theta(i))+phi_f(i));
% Draw Back Platfrom
trans = makehgtform('translate',[(xpos(i)-l*cos(theta(i))) (ypos(i)l*sin(theta(i))) 0]);
rotz = makehgtform ('zrotate',((theta(i))+phi_b(i)));
end
% Camera Position
camzoom(1);
camtarget([xpos(i),ypos(i),0]);
campos([xpos(i)-7,ypos(i)-7,10]);
% pause(animation_pause)
% Crumb Trail
crumb_counter = crumb_counter+1;
if crumb_counter == crumb_frequency
r(1) = surface(xc+xpos(i),yc+ypos(i),.05*zc,'FaceColor','black');
rr = hgtransform('Parent',ax);
set(r,'Parent',rr)
drawnow
hold on
crumb_counter = 0;
end
end
11.
%
%
%
%
%
Bifurcation Test
Title: Bifurcation test
Filename: bifurcation.m
Purpose: Performs the bifurcation tests
clc;
clear all;
close all;
a1 = 0.3;
a2 = -0.3;
ar = 0.7;
omega_1 = 1;
omega_2 = 1;
omega_r = 1;
Bf = 0;
Bb = 0;
Br = 0;
for omega_2 = 0:0.01:2
[t,x]=ode45(@snakeboard_differential,tspan,x0,[],m,l,J,Jr,Jp,a1,a2,ar,omega_1,omega_2
,omega_r,Bf,Bb,Br);
%% Create Plots
f = figure('Position',[scrsz(1)+scrsz(3)/4 scrsz(2)+scrsz(4)/6 scrsz(3)/2
scrsz(4)/1.5]);
subplot(2,1,1)
plot(t,phi_f,t,phi_b,t,delta),xlabel('Time (seconds)'),ylabel('Angle
(rads)'),legend('Front Platform Angle','Back Platform Angle','Rotor Angle',1),title('Controlled Parameters');
subplot(2,1,2)
plot(x(:,1),x(:,2)),xlabel('x (m)'),ylabel('y (m)'),title('Trajectory');
str = ['omega_f=',num2str(omega_1),', omega_b=',num2str(omega_2),',
omega_r=',num2str(omega_r),];
v = get(gca);
lh = line([0 0 NaN v.XLim],[v.YLim NaN 0 0 ]);
set(lh,'Color',[.25 .25 .25],'LineStyle',':');
uicontrol('Style', 'text',...
'String', str,... %replace something with the text you want
'Units','normalized',...
'Position', [0.8 0.65 0.1 0.1]);
filename = sprintf('a1_%4.3f-a2_%4.3f-ar_%4.3f-omega1_%4.3f-omega2_%4.3fomegar_%4.3f-Bf_%4.3f-Bb_%4.3fBr_%4.3f.png',a1,a2,ar,omega_1,omega_2,omega_r,Bf,Bb,Br);
print(f,'-dpng',filename);
filename = sprintf('a1_%4.3f-a2_%4.3f-ar_%4.3f-omega1_%4.3f-omega2_%4.3fomegar_%4.3f-Bf_%4.3f-Bb_%4.3fBr_%4.3f.png',a1,a2,ar,omega_1,omega_2,omega_r,Bf,Bb,Br);
dpi = 200;
mag = dpi / get(0, 'ScreenPixelsPerInch');
set(gca, 'Color', 'none'); % Sets axes background
export_fig(gcf,'png','bounds',filename,'-transparent', sprintf('-m%g', mag));
close;
end
12.
%
%
%
%
%
Motion Planning (Version 3)
Title: Motion Planning
Filename:motion_planning.m
Purpose: Calculates parameters to satisfy user’s requirements
clc;
clear all;
close all;
%% Prompt user for input
prompt = {'Enter x-displacement (metres):','Enter duration (seconds):','Enter
accuracy required (m):'};
dlg_title = 'Snakeboard Motion Planning';
num_lines = 1;
def = {'30','60','0.01'};
answer = inputdlg(prompt,dlg_title,num_lines,def);
tic;
x_displacement_required = str2num(answer{1});
duration = str2num(answer{2});
x_displacement_accuraccy = str2num(answer{3});
a1 = 0.3;
a2 = -0.3;
ar = 0.7;
omega = 0; % Starting omega
omega_increment = 0.5;
minimum_omega_increment = 0.1*10^-10;
monitor = [0 0 0];
iteration = 0;
Bf = 0;
Bb = 0;
Br = 0;
achieved_distance = 0;
x_distance = 0;
time_step = 0.1; % The resolution of the differentiation
tspan1 = 0:time_step:duration/2;
tspan2 = duration/2:time_step:duration;
while achieved_distance < 1
omega = omega + omega_increment;
current_monitor = [iteration omega x_distance];
monitor = [monitor;current_monitor];
iteration = iteration + 1;
x0=[0;0;0;0];
[t,x]=ode45(@snakeboard_differential_equations,tspan1,x0,[],m,l,J,Jr,Jp,a1,a2,ar,omeg
a,omega,omega,Bf,Bb,Br);
x0 = x(end,:);
x0 = x0.';
xpos = x(:,1);
ypos = x(:,2);
theta = x(:,3);
v = x(:,4);
[t,x]=ode45(@snakeboard_differential_equations,tspan2,x0,[],m,l,J,Jr,Jp,a1,a2,ar,omega,omega,omega,Bf,Bb,Br);
xpos = [xpos;x(:,1)];
ypos = [ypos;x(:,2)];
theta = [theta;x(:,3)];
v = [v;x(:,4)];
if (x_distance > x_displacement_required) && (x_distance <
x_displacement_required+x_displacement_accuraccy)
elseif ((x_distance > x_displacement_required) && (omega_increment <
minimum_omega_increment))
disp('Solution is not converging to the accuracy required.');
disp('Shown below are controlled parameters that may still be of interest');
elseif (x_distance > x_displacement_required)
omega = omega - omega_increment; % This take omega back to a value that is
definitely less than the required x displacement
omega_increment = omega_increment/2; % Decrease the size of omega_increment so a
more accurate answer is achieved
end
end
solve_time = toc;
tspan1 = tspan1.';
tspan2 = tspan2.';
tspan = [tspan1;tspan2(:,1)];
phi_f_1 = a1*sin(omega*tspan1+Bf); % Front Platorm Angle
phi_b_1 = a2*sin(omega*tspan1+Bb); % Back Platform Angle
delta_1 = ar*sin(omega*tspan1+Br); % Rotor Angle
phi_f_2 = a1*sin(omega*tspan2+Bf); % Front Platorm Angle
phi_b_2 = a2*sin(omega*tspan2+Bb); % Back Platform Angle
delta_2 = -ar*sin(omega*tspan2+Br); % Rotor Angle
phi_f = [phi_f_1;phi_f_2(:,1)];
phi_b = [phi_b_1;phi_b_2(:,1)];
delta = [delta_1;delta_2(:,1)];
snakeboard = zeros(numel(xpos),8);
snakeboard(:,1)
snakeboard(:,2)
snakeboard(:,3)
snakeboard(:,4)
snakeboard(:,5)
snakeboard(:,6)
snakeboard(:,7)
snakeboard(:,8)
=
=
=
=
=
=
=
=
tspan;
xpos;
ypos;
theta;
v;
phi_f;
phi_b;
delta;
line_0 = ['User specified ',num2str(x_displacement_required),'m in
',num2str(duration),' seconds with an accuracy of
',num2str(x_displacement_accuraccy),'m.'];
line_1 = ['For 0 to ',num2str(duration/2), ' seconds:'];
line_2 = ['Then, from ',num2str(duration/2), ' to ',num2str(duration),' seconds:'];
line_3 = ['Distance travelled: ',num2str(x_distance), ' (m)'];
line_4 = ['Calculation time: ',num2str(solve_time), ' (s)'];
a = '%4.12f';
disp(line_0);
disp(line_1);
fprintf('\t omega_f = '); disp(num2str(omega,a));
fprintf('\t omega_b = '); disp(num2str(omega,a));
fprintf('\t omega_r = '); disp(num2str(omega,a));
fprintf('\t a_f = '); disp(num2str(a1));
fprintf('\t a_b = '); disp(num2str(a2));
fprintf('\t a_r = '); disp(num2str(ar));
fprintf('\t B_f = '); disp(num2str(Bf));
fprintf('\t B_b = '); disp(num2str(Bb));
fprintf('\t B_r = '); disp(num2str(Br));
disp(line_2);
fprintf('\t omega_f = '); disp(num2str(omega,a));
fprintf('\t omega_b = '); disp(num2str(omega,a));
fprintf('\t omega_r = '); disp(num2str(omega,a));
fprintf('\t a_f = '); disp(num2str(a1));
fprintf('\t a_b = '); disp(num2str(a2));
fprintf('\t a_r = -'); disp(num2str(ar));
fprintf('\t B_f = '); disp(num2str(Bf));
fprintf('\t B_b = '); disp(num2str(Bb));
fprintf('\t B_r = '); disp(num2str(Br));
disp(line_3);
disp(line_4);
plot(monitor(:,1),monitor(:,3),'s-'),xlabel('Iteration'),ylabel('x-displacement
(m)');
13.
Stable Resonance Gait
% Title: Stable Resonance Gait
% Filename:stable.m
% Author: Jonathan Jamieson
% Date: September 2011 – April 2012
% Purpose: Caclulates a stable resonance gait
clc;
clear all;
close all;
min_a_p = 0.0;
max_a_p = 0.8;
min_a_r = 0.0;
max_a_r = 0.7;
duration = 1*2*pi;
omega = 1;
a_increment = 0.01;
time_increment = (2*pi)/omega;
a_p_range = min_a_p:a_increment/10:max_a_p;
a_r_range = min_a_r:a_increment*10:max_a_r;
Bf = 0;
Bb = 0;
Br = 0;
x0=[0;0;0;0];
time_step = 0.01; % The resolution of the differentiation
current_time = 0;
snakeboard = [0 0 0 0 0 0 0 0 0 0];
while current_time <= duration
current_row = 1;
z = zeros(numel(a_p_range)*numel(a_r_range),3);
tspan = current_time:time_step:current_time+time_increment;
for i_a_p = 1:numel(a_p_range)
for i_a_r = 1:numel(a_r_range)
a1 = a_p_range(i_a_p);
a2 = -a_p_range(i_a_p);
ar = a_r_range(i_a_r);
[t,x]=ode45(@snakeboard_differential_equations_input_2,tspan,x0,[],m,l,J,Jr,Jp,a1,a2,
ar,omega,omega,omega,Bf,Bb,Br);
xpos = x(:,1);
z(current_row,1) = a1;
z(current_row,2) = ar;
z(current_row,3) = x_distance;
current_row = current_row +1;
end
end
[m,idx] = max(z(:,3)); % idx is the row which constains largest value for x
a1 = z(idx,1);
a2 = -z(idx,1);
ar = z(idx,2);
[t,x]=ode45(@snakeboard_differential_equations_input_2,tspan,x0,[],m,l,J,Jr,Jp,a1,a2,
ar,omega,omega,omega,Bf,Bb,Br);
xpos = x(:,1);
ypos = x(:,2);
theta = x(:,3);
v = x(:,4);
phi_f = a1*sin(omega*tspan+Bf);
phi_b = a2*sin(omega*tspan+Bb);
delta = ar*sin(omega*tspan+Br);
a1 = (ones(1,numel(t))*a1).';
ar = (ones(1,numel(t))*ar).';
snakeboard_append(:,1) = tspan;
snakeboard_append(:,2) = xpos;
snakeboard_append(:,3) = ypos;
snakeboard_append(:,4) = theta;
snakeboard_append(:,5) = v;
snakeboard_append(:,6) = phi_f;
snakeboard_append(:,7) = phi_b;
snakeboard_append(:,8) = delta;
snakeboard_append(:,9) = a1;
snakeboard_append(:,10) = ar;
snakeboard = [snakeboard;snakeboard_append];
current_time = current_time+time_increment;
x0 = x(end,:);
x0 = x0.';
end
14.
Static Overlay
% Title: Static Overlay
% Filename:static_overlay.m
% Author: Jonathan Jamieson
% Date: September 2011 – April 2012
% Purpose: Creates a static over plot from a recorded snakeboard path
show_trace = 0; % 0,
zoomed_in = 0;
trace_frequency = 20;
show_trace_fig = figure('Position',[scrsz(1)+scrsz(3)/4 scrsz(2)+scrsz(4)/6
scrsz(3)/1.1 scrsz(4)/1.5]);
xpos = snakeboard(:,2);
ypos = snakeboard(:,3);
theta = snakeboard(:,4);
v = snakeboard(:,5);
phi_f = snakeboard(:,6);
phi_b = snakeboard(:,7);
delta = snakeboard(:,8);
show_trace_fig = gca;
if min(xpos) < min(ypos)
min_grid = min(xpos);
else
min_grid = min(ypos);
end
if max(xpos) > max(ypos)
max_grid = max(xpos);
else
max_grid = min(ypos);
end
xlim(show_trace_fig, [((min_grid-10*l)) (max_grid+10*l)] );
camzoom(1);
axis equal
hold on
trace_counter_moving = 0;
plot(xpos,ypos,':','LineWidth',2)
xlabel('x (m)'),ylabel('y (m)'),title('Static Overlay');
hold off
trace_counter = 0;
trace_counter = trace_counter + 1;
if trace_counter == trace_frequency
% drawnow;
c1_x
c1_y
c2_x
c2_y
=
=
=
=
l*cos(theta(i))+xpos(i);
l*sin(theta(i))+ypos(i);
-l*cos(theta(i))+xpos(i);
-l*sin(theta(i))+ypos(i);
fp1_x
fp1_y
fp2_x
fp2_y
=
=
=
=
l*cos(theta(i))
l*sin(theta(i))
l*cos(theta(i))
l*sin(theta(i))
bp1_x
bp1_y
bp2_x
bp2_y
=
=
=
=
-l*cos(theta(i))
-l*sin(theta(i))
-l*cos(theta(i))
-l*sin(theta(i))
r1_x
r1_y
r2_x
r2_y
=
=
=
=
+
+
+
+
0.25*l*cos(theta(i)+phi_f(i)+pi/2)+ xpos(i);
0.25*l*sin(theta(i)+phi_f(i)+pi/2)+ ypos(i);
0.25*l*cos(theta(i)+phi_f(i)+3*pi/2)+ xpos(i);
0.25*l*sin(theta(i)+phi_f(i)+3*pi/2)+ ypos(i);
+
+
+
+
0.25*l*cos(theta(i)+phi_b(i)+pi/2)+ xpos(i);
0.25*l*sin(theta(i)+phi_b(i)+pi/2)+ ypos(i);
0.25*l*cos(theta(i)+phi_b(i)+3*pi/2)+ xpos(i);
0.25*l*sin(theta(i)+phi_b(i)+3*pi/2)+ ypos(i);
0.5*l*cos(theta(i) + delta(i)) + xpos(i);
0.5*l*sin(theta(i) + delta(i)) + ypos(i);
-0.5*l*cos(theta(i) + delta(i)) + xpos(i);
-0.5*l*sin(theta(i) + delta(i)) + ypos(i);
c_handle = line([c1_x c2_x],[c1_y c2_y],'Color','black','LineWidth',1);
fp_handle = line([fp1_x fp2_x],[fp1_y fp2_y],'Color','red','LineWidth',1);
bp_handle = line([bp1_x bp2_x],[bp1_y bp2_y],'Color','red','LineWidth',1);
r_handle = line([r1_x r2_x],[r1_y r2_y],'Color','green','LineWidth',1);
if zoomed_in == 1
camzoom(1);
campos([xpos(i),ypos(i),50]);
end
pause(0.1);
trace_counter = 0;
end
end
15.
%
%
%
%
%
Animated 2D
Title: Animated 2D
Filename:animated_2d.m
Purpose: Creates an animation of a previously recorded snakeboard path
zoomed_in_moving = 1
show_trace_moving_fig = figure('Position',[scrsz(1)+scrsz(3)/4 scrsz(2)+scrsz(4)/6
scrsz(3)/2 scrsz(4)/1.5]);
xpos = snakeboard(:,2);
ypos = snakeboard(:,3);
theta = snakeboard(:,4);
velocity = snakeboard(:,5);
phi_f = snakeboard(:,6);
phi_b = snakeboard(:,7);
delta = snakeboard(:,8);
show_trace_handle_moving = gca;
min_y
min_x
max_y
max_x
=
=
=
=
min(ypos);
min(xpos);
max(ypos);
max(xpos);
if min_x < min_y
min_grid = min_x;
else
min_grid = min_y;
end
if max_x > max_y
max_grid = max_x;
else
max_grid = max_y;
end
xlim(gca, [((min_grid-50*l)) (max_grid+50*l)] );
camzoom(1);
axis equal
hold on
trace_counter_moving = 0;
plot(xpos,ypos,':','LineWidth',2)
hold off
c_handle = line([0 0],[0 0],'Color','black','LineWidth',4);
fp_handle = line([0 0],[0 0],'Color','red','LineWidth',2);
bp_handle = line([0 0],[0 0],'Color','red','LineWidth',2);
r_handle = line([0 0],[0 0],'Color','green','LineWidth',1);
c1_x
c1_y
c2_x
c2_y
=
=
=
=
l*cos(theta(i))+xpos(i);
l*sin(theta(i))+ypos(i);
-l*cos(theta(i))+xpos(i);
-l*sin(theta(i))+ypos(i);
fp1_x
fp1_y
fp2_x
fp2_y
=
=
=
=
l*cos(theta(i))
l*sin(theta(i))
l*cos(theta(i))
l*sin(theta(i))
+
+
+
+
bp1_x
bp1_y
bp2_x
bp2_y
=
=
=
=
-l*cos(theta(i))
-l*sin(theta(i))
-l*cos(theta(i))
-l*sin(theta(i))
0.25*l*cos(theta(i)+phi_f(i)+pi/2)+ xpos(i);
0.25*l*sin(theta(i)+phi_f(i)+pi/2)+ ypos(i);
0.25*l*cos(theta(i)+phi_f(i)+3*pi/2)+ xpos(i);
0.25*l*sin(theta(i)+phi_f(i)+3*pi/2)+ ypos(i);
+
+
+
+
0.25*l*cos(theta(i)+phi_b(i)+pi/2)+ xpos(i);
0.25*l*sin(theta(i)+phi_b(i)+pi/2)+ ypos(i);
0.25*l*cos(theta(i)+phi_b(i)+3*pi/2)+ xpos(i);
0.25*l*sin(theta(i)+phi_b(i)+3*pi/2)+ ypos(i);
r1_x = 0.5*l*cos(theta(i) + delta(i)) + xpos(i);
r1_y = 0.5*l*sin(theta(i) + delta(i)) + ypos(i);
r2_x = -0.5*l*cos(theta(i) + delta(i)) + xpos(i);
r2_y = -0.5*l*sin(theta(i) + delta(i)) + ypos(i);
set(c_handle,'XData',[c1_x c2_x],'YData',[c1_y c2_y]);
set(fp_handle,'XData',[fp1_x fp2_x],'YData',[fp1_y fp2_y]);
set(bp_handle,'XData',[bp1_x bp2_x],'YData',[bp1_y bp2_y]);
set(r_handle,'XData',[r1_x r2_x],'YData',[r1_y r2_y]);
if zoomed_in_moving == 1
campos([xpos(i),ypos(i),25]);
end
pause(0.05);
end
Appendix III
– Microcontroller Code
main:
readadc10 1,w1 'Front platform
readadc10 2,w2 'Back platform
readadc10 4,w3 ' Rotor
sertxd (#w1,",",#w2,",",#w3,cr)
pause 2
goto main

Dynamics and Simulation of a Snakeboard‟s Motion

Transcription

Similar documents

The Location and Kings of Paradan - people

here - TimeZone

TimeZone: Omega => 50th anniversary speedmaster

Number of residents served

Gage 78 Earth Metal™ Dimensional Surfacing

Psychology Department Newsletter

Parker Flow Meter and Valve Solutions

The Bridge to Our Future

Snakeboard Motion Planning With Local Trajectory Information

Manual - Welcome to Emerson Process Management Documentation

a practical approach for estimation of the average

December 2, 2015 - Housing Opportunities Commission

Introduction to Differential Equations