UMN-AML-TR-00-02 - Department of Mechanical Engineering

Transcription

Technical Report
Design of Meso-Scale Robotic Systems with Miniature Actuators
Kemal Berk Yesin
UMN-AML-TR-00-02
Advanced Microsystems Laboratory
Department of Mechanical Engineering
University of Minnesota
111 Church St. SE
Minneapolis, MN 55455
April 2000

2000 University of Minnesota
This report is based upon work supported by the Defense Advanced Research Projects Agency, Electronics
Technology Office (Distributed Robotics Program), ARPA Order No. G155, Program Code No. 8H20, Issued by DARPA/CMD under Contract #MDA972-98-C-0008.
Abstract
In this report, design of meso-scale robotic systems using miniature actuators is investigated. Specifically, design of an active video module for a meso-scale mobile reconnaissance robot is discussed. The small size of the robot presents strict requirements on size and power consumption of
the module. Available technologies for video sensors, wireless transmitters and miniature actuators
are discussed with an emphasis on their applicability to meso-scale mobile systems having limited
volume and working on battery power. Alternative mechanical designs for the pan-tilt mechanism
of the module are presented. Computer vision techniques are implemented to perform visual tracking of targets and dynamic characteristics of the system are experimentally evaluated. Finally, a
simple motion detection and tracking algorithm that was developed to track people moving inside
a room is presented.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Distributed Robotics Using Reconfigurable Robots . . . . . . . . . .
2.1 The Ranger . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 The Scout . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3 Video Reconnaissance Module . . . . . . . . . . . . . . . .
3. Video Sensor and Wireless Transmission . . . . . . . . . . . . . . .
3.1 Video Sensor . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1 CCD sensors . . . . . . . . . . . . . . . . . . . . .
3.1.2 CMOS sensors . . . . . . . . . . . . . . . . . . . .
3.2 Wireless transmitter . . . . . . . . . . . . . . . . . . . . . .
4. Actuators for Miniature Systems . . . . . . . . . . . . . . . . . . . .
4.1 Piezoelectric Actuators . . . . . . . . . . . . . . . . . . . .
4.2 Shape Memory Alloy (SMA) Actuators . . . . . . . . . . .
4.3 Electro-Mechanical Actuators . . . . . . . . . . . . . . . .
5. DESIGN WITH MINIATURE ACTUATORS . . . . . . . . . . . . .
5.1 Specifications for the Video Reconnaissance Module . . . .
5.2 Alternative Designs . . . . . . . . . . . . . . . . . . . . . .
5.3 First Generation Video Reconnaissance Module (VRM-1) .
5.3.1 Mechanical construction . . . . . . . . . . . . . . .
5.3.2 Driver electronics . . . . . . . . . . . . . . . . . .
5.3.3 Test results . . . . . . . . . . . . . . . . . . . . . .
5.4 Second Generation Video Reconnaissance Module (VRM-2)
5.4.1 Mechanical design . . . . . . . . . . . . . . . . . .
6. Active Vision with the Video Module . . . . . . . . . . . . . . . . .
6.1 Experimental set-up . . . . . . . . . . . . . . . . . . . . . .
6.2 Motion Detection and Tracking . . . . . . . . . . . . . . . .
7. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8. References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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vi
List of Figures
Figure 1: Micro-aerial vehicle (a) and miniature motor (b) . . . . . . . . . . . . . . . . . . . 1
Figure 2: Distributed robotic system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Figure 3: The Ranger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Figure 4: The Scout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Figure 5: Scout jumping over obstacle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Figure 6: Wireless Image Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Figure 7: CCD camera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Figure 8: Video transmitter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Figure 9: Dipole orientation in PZT ceramic . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Figure 10: Stacked piezoactuator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Figure 11: Ultrasonic Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Figure 12: Shape Memory Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Figure 13: Electric Motor Types. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Figure 14: DC motor operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Figure 15: Permanent magnet motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Figure 16: Brushless Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Figure 17: DC motor torque-speed curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Figure 18: Scout payload volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Figure 19: Video camera. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Figure 20: Static camera position and tilt control by spring arm . . . . . . . . . . . . . . . . 23
vii
Figure 21: Smoovy motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Figure 22: Motor-gearbox assembly. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Figure 23: VRM-1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Figure 24: VRM-1 in up, tilted and panned configuration . . . . . . . . . . . . . . . . . . . 29
Figure 25: Smoovy motor speed-torque characteristic . . . . . . . . . . . . . . . . . . . . . 30
Figure 26: Square wave 3 phase driver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Figure 27: Pulse Width Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Figure 28: System diagram for scout-VRM interface . . . . . . . . . . . . . . . . . . . . . . 31
Figure 29: Dual action mechanism operation . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Figure 30: VRM-2 cross-section. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Figure 31: VRM-2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Figure 32: VRM-2 up, tilted and panned . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Figure 33: Experimental setup. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Figure 34: SSD error surface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Figure 35: Pinhole camera model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Figure 36: Controller system diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Figure 37: Step response of VRM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Figure 38: Visual targets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Figure 39: System response to sinusoidal inputs . . . . . . . . . . . . . . . . . . . . . . . . 42
Figure 40: Frequency response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Figure 41: Motion detection algorithm flow chart. . . . . . . . . . . . . . . . . . . . . . . . 45
Figure 42: Motion tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
viii
List of Tables
Camera Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Miniature Video Transmitters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
NiTi Alloy Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Comparative Summary of Actuators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Miniature Motors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Smoovy Motor Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
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Figure Credits
Figure 1a is courtest of Aeronvironment.
Figure 10 is courtesy of Physic Instrumente.
Figures 1b, 21, 22, 25 are courtesy of RMB Miniature Bearings.
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1
1. Introduction
From giant powerplant turbines to silicon micromotors, a broad range of actuators have been realized for an even broader range of applications. A new class of miniature actuators with millimeter
to centimeter dimensions has recently emerged. Improvements in these actuators and their increased availability has generated a variety of applications.
Many diverse fields will benefit from these miniature actuators such as portable devices, medical
systems and robotics. For example, a centimeter size gear-pump has been developed by MEMStek
for medical applications. This device uses a miniature electric motor for actuation [3]. Micro-aerial
vehicles built by Aeronvironment are remote controlled reconnaissance airplanes about six inches
in length that use miniature electric motors for control surface actuation [5]. Figure 1 shows a micro-aerial vehicle and a miniature electric motor.
(a)
(b)
Figure 1: Micro-aerial vehicle (a) and miniature motor (b)
This report investigates a new class of miniature sized electromagnetic actuators for meso-scale
electro-mechanical systems. The actuators form a key component of a miniature robotic system.
Several of these miniature robots combine with medium sized carrier robots to create a novel distributed robotic system. The distributed robotic system is itself a joint research project between the
University of Minnesota, MTS Systems Corp., and Honeywell Inc.
The main objective of the system is reconnaissance with a group of remotely operated mobile robots that are linked to one other and to an operator via a wireless communication network. The system is primarily developed to operate in an urban environment. A number of medium sized carrier
robots are deployed to the area of reconnaissance, each carrying up to twelve miniature mobile robots. These miniature robots are deployed by the carriers through a special launching mechanism
2
Tech Report UMN-AML-00-02
and each contain sensory devices such as audio, video and vibration sensors. This way a distributed
sensory network is established.
This report specifically discusses the design and development of a video reconnaissance module,
a sensory component carried by the miniature robots. The module consists of a video camera with
wireless image transmission and pan-tilt capabilities. Miniature electromagnetic actuators are used
on the pan-tilt mechanism.
Chapter 2 introduces the distributed robotics project, briefly summarizes its components and introduces the video reconnaissance module. Chapter 3 describes the video camera and wireless video
transmitter components of the module and summarizes available technologies. Chapter 4 discusses
three common forms of actuation that are suitable for miniaturization, emphasizing their applicability to mobile robots of this scale. Chapter 5 describes alternative mechanical designs for the video reconnaissance module, discusses design issues with miniature actuators and presents test
results. Finally, Chapter 6 integrates the pan-tilt device with real-time computer vision algorithms
to form a closed-loop visual servoing system for autonomously tracking observed motion.
3
2. Distributed Robotics Using Reconfigurable Robots
A joint project between the University of Minnesota, MTS Systems Corporation and Honeywell
Inc., called “Distributed Robotics Using Reconfigurable Robots”, is developing a novel system of
mobile robots for distributed reconnaissance [10]. The main objective of this project is to create a
fleet of medium and miniature sized mobile robots for distributed reconnaissance. The system is
primarily intended for use in an urban environment. The medium sized robots, called “Rangers”
carry and deploy the miniature robots called “Scouts”. All robots have wireless communication capability. Due to their small size the scouts have limited communications range, and exchange information with human operators via the rangers. Figure 2 illustrates this overall concept.
OPERATOR
Ranger
scout
scout
scout
scout
scout
scout
scout
Ranger
Figure 2: Distributed robotic system
2.1
The Ranger
Rangers are medium sized (65x62x36 cm) all-terrain mobile robots with wireless ethernet and video links. Their main purpose is to carry and deploy the scouts and to provide a long range communication link between the scouts and the operator. Each ranger can carry twelve scouts in a special
4
magazine and deploy them through a launcher. The launcher uses a spring coil mechanism with a
motorized tilt. Figure 3 illustrates a ranger with the launcher on top.
Figure 3: The Ranger
2.2
The Scout
Scouts are cylindrical in shape with an outer diameter of 40 mm and length of 110 mm. The wheels
at both ends are turned independently by small dc motors for mobility. A small spring arm protrudes from the side and can be wrapped around the cylindrical shell by a separate winch mechanism controlled by a third motor. Upon quick release of the spring arm a hopping action is
achieved. This mode of locomotion is intended mainly to overcome large obstacles and climb
stairs. Figure 4 shows a scout. Figure 5 shows the scout hopping over an obstacle. The scout is able
to jump over obstacles 25 cm high.
The scout has two Scenix microprocessors (8-bit, 50MHz), one for data processing and the other
for wireless communications protocol, driver electronics for its motors, a magnetometer and a tiltmeter for direction and roll sensing. Nine 3V lithium batteries are used to supply power. The quiescent power consumption is 1.7 W, however it increases to 3.4 W while winching in the spring
arm.
5
Figure 4: The Scout
Figure 5: Scout jumping over obstacle
2.3
Video Reconnaissance Module
The basic scout robot described above can be reconfigured with various types of modular sensors
depending on its mission such as vibration, chemical, audio and video sensors. The most important
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reconnaissance mode envisioned for the scouts is visual. Therefore, the key sensing component developed for the scout is a video reconnaissance module.
The basic purpose of the video reconnaissance module is to provide visual feedback from the scout
to the operator. This definition identifies two main components of the system, a vision sensor for
acquiring images and means for their wireless transmission. Another requirement is dictated by the
dynamic nature of the system and its operation. Unlike the common surveillance cameras on buildings which monitor a fixed location, the scout’s vision sensor must be able to orient itself to explore
its surroundings. A mechanism using miniature actuators are used for this purpose. Remaining
chapters of this report will investigate the components and design of the video reconnaissance
module.
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3. Video Sensor and Wireless Transmission
Two basic components of the video reconnaissance module are the video sensor and wireless transmitter. A lens system focuses light rays from the viewed scene onto the sensor. A video signal representing the image is produced by the sensor and is sent to the wireless transmitter. The transmitter
emits an RF signal that represents the video signal. A receiver some distance away receives the RF
signal and extracts the video signal. This video signal is used to generate the image on a display
device. Figure 6 illustrates the process.
Video
RF
Signal
Signal
Lens
System
Video
Sensor
Wireless
Transmitter
Video
Signal
Receiver
Display Device
Figure 6: Wireless Image Transmission
3.1
Video Sensor
Early image sensors were vacuum tube type devices with significant weight and size. By the introduction of CCD technology about twenty-five years ago there has been a sharp decrease in their
cost, size and power consumption. Recently, CMOS technology has been used to fabricate image
sensors resulting in further reduction in all three of these parameters. These two dominating technologies are introduced and compared below.
3.1.1 CCD sensors
CCD stands for Charge-Coupled-Device. The front face of the sensor is a rectangular grid array of
small photodiode elements that are used to convert incident light (photons) to electrical charge
(electrons). Integrated for a small duration, these charges represent the light intensity on that photodiode or pixel (picture-element). The individual charges on each photodiode are then transferred
in a clocked fashion towards an amplifier stage. This way the light intensity pattern (the image) on
the sensor plate is “scanned” row by row. When all rows are scanned a frame has been generated
and the scanning of the next frame starts. Under the NTSC standard for video, 30 frames are generated each second. The resulting video signal also contains synchronization signals to label the
beginnings of rows and frames.
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Image sensors are manufactured in standard sizes and named by the approximate diagonal size of
the sensor plate. A typical 1/3 inch CCD sensor will have a sensor plate of about 4.82mm x 3.64
mm in size and contain 492 rows and 512 columns of pixels [7]. The total number of pixels and the
sensor size determine the resolution of the sensor, therefore the larger the number of pixels per unit
area the higher the resolution.
As described above, the CCD sensor requires other functions to operate, such as clocks, timers and
amplifier stages. It is feasible but not economical to integrate these other functions into a single
circuit using the CCD manufacturing technology [4]. Therefore, CCD based video cameras require
external circuitry with the image sensor. Nevertheless, small single board CCD cameras with simple lens systems are widely available on the market. Figure 7 shows such a camera. The board size
of the camera shown is 32x32 mm.
Figure 7: CCD camera
The working principle for a CCD camera is similar for both grayscale and color sensors. Advanced
color systems have optical filters to separate red, green and blue components of the image and then
use three separate CCD sensors to generate a color signal. Simple single sensor systems microfabricate the filters onto individual pixels of the sensor.
Single board CCD cameras typically require a single 5 V DC power supply to operate and consume
100 to 150 mA current.
An important feature found on many of the CCD cameras is automatic exposure control circuit.
This circuit adjusts the integration time of the pixels (the duration while the photons hit the pixels
and charges are collected before they are sampled and flushed) and eliminates the need for external
mechanical shutter components. In other words, the camera electronically adjusts to ambient lighting conditions and no mechanical aperture in the lens system is needed.
3.1.2
9
CMOS sensors
CMOS (Complementary Metal-Oxide Semiconductor) technology is widely used for semiconductor device manufacturing. It has recently been applied to image sensors resulting in considerable
improvements in size, cost and power consumption.
Both CMOS and CCD sensors use the same photoconversion process to convert incident photons
to electrical charge. For the CMOS sensor, however, these charges are not transferred from the pixels but amplified at the point of collection by dedicated CMOS transistors [4]. Thus, every pixel
has its own amplification circuitry.
The two main advantages of CMOS sensors over CCDs result from the wide application of CMOS
technology in industry. In addition to reducing the manufacturing cost, the use of well developed
CMOS technology facilitates the integration of all camera functions into a single VLSI package.
Therefore, several single-chip video cameras are available on the market at very low costs. Additionally, they typically consume three to five times less power than similar CCD based cameras.
CCD sensors are known to have better image quality compared to CMOS sensors. One major
problem with the CMOS sensor was the fixed pattern noise resulting from the unmatched transistor
characteristics at each pixel [4]. However, this problem has been greatly reduced. Today, CMOS
sensor manufacturers claim to have reached the image quality of common CCD sensors.
An analog video signal is not the only output option on single-chip cameras. Several manufacturers
make sensors with digital output. These are especially useful with PC based systems since the necessary digitization is already done in the camera. However, transmission of digital images requires
a high bandwidth communication link. The current wireless link on the scout is limited to 2.4 kbps
which is too low for the real-time video requirements dictated by the distributed robotics project.
As previously explained, CMOS sensor based cameras have advantages over the CCDs in term of
size, power consumption and cost. Therefore, a CMOS sensor based camera system was selected
for the video reconnaissance module. The monochrome sensor selected is a BV5016 by OmniVision. A pinhole lens with a 5.7 mm focal length is used to focus the image onto the video sensor.
The resulting sensor-lens package is approximately 15x15x16 mm in size and weighs 3.5 grams.
Table 1 summarizes the specification
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Table 1: Camera Specifications
3.2
Sensor type
Single-chip monochrome
CMOS sensor with 320x240
pixels
Size
15 x 15 x 16 mm
Power consumption
20 mA at 6-9 VDC
Output
Composite video signal, 2 V p-p
at 30 frame/second
Lens
Pinhole lens 5.7 mm focal length
Wireless transmitter
A number of wireless video transmitters are available on the market with various sizes and power
consumption values. Table 2 summarizes the specifications of some commercially available miniature transmitters.
Table 2: Miniature Video Transmitters
Model/
Manufacturer
Size [mm]
Frequency
Range
Specification
Power
Vid-Link 100
Virtual Spy
28 x 20 x 10
434 MHz
150 m
35 mA, 9 V
MV915
Micro Video Products
24 x 17 x 8
915 MHz
300 m
30 mA, 9 V
MP-1 Microplate
Supercircuits
50 x 32 x 4
434 MHz
200 m
100 mA, 12 V
T900V
Applied-wireless
40 x 20 x 7
900 MHz
150 m
20 mA, 3 V
An important design criterion for the transmitter is the operating frequency. Commercial transmitters of small size operate in three different bands reserved by the FCC for unlicensed operation,
namely the 440 MHz ATV (Amateur TV) band, the 900 MHz ISM (Industrial , Scientific, Medical)
band, and the 2.4 GHz band. Generally, the higher the frequency the longer the transmission range
for a given transmitting power. On the other hand, higher frequency waves concentrate more of
their power in one direction. Therefore high frequency transmitters perform better in line-of-sight
applications (i.e. when the transmitter antenna and the receiver antenna are in line-of-sight) but are
more sensitive to alignment of the transmitter and receiver antennas.
Most transmitters use Frequency Modulation (FM) while some use Amplitude Modulation (AM).
In general, FM systems have less noise and signal degradation compared to AM systems.
11
A common reason for signal degradation of wireless transmission occurs when the transmitter signal is reflected from surrounding surfaces and reaches the receiver along different paths, resulting
in multiple signals that are slightly out of phase with one another. Some advanced transmitters use
phase-locked-loop (PLL) circuitry to reject the out of phase signals, however, this additional circuitry increases the size and power consumption of the transmitter.
A 900 Mhz transmitter from Micro Video Products was chosen for the video reconnaissance module. It is 24x17x8 mm in size and consumes 30 mA at 9V. Its range was tested to be 150-200 ft.
line-of-sight, indoors. However, the structure of the building can effect this range. Figure 8 shows
the transmitter.
Figure 8: Video transmitter
12
4. Actuators for Miniature Systems
With the advent of technologies like VLSI and SMT (Surface-Mount-Technology), there has been
a great reduction in size of electronics which has caused a further drive to miniaturize many products. The leading edge in this trend is MEMS (Micro Electro-Mechanical Systems) technology,
which is itself an offshoot of silicon manufacturing. MEMS fabrication uses photo-chemical processes similar to the VLSI industry for creating mechanical structures with millimeter to micron
dimensions. Almost all successful commercial applications of MEMS are sensors, though research
is performed worldwide to develop micro-actuation mechanisms. However difficulties exist in the
miniaturization of actuators due to both manufacturing issues and scaling effects. As the parts get
smaller, inertial (volumetric) forces tend to loose their dominance over surface effect forces such
as electrostatics [25].
Despite these difficulties many attempts to develop microscale actuators have been pursued. Microactuators can be classified into two major groups [11] : those using electrostatic and electromagnetic forces (for example electric motors) and those that use a functional element. Two well known
examples of the latter type are actuators using piezo-ceramic materials and those using shape memory alloys. These actuators can also be used in meso-scale systems however, their effectiveness is
naturally quite different at this scale than at the microscale. Additionally, the power requirements
and control of these actuators will be different and these differences become even more important
in miniature robotic systems with limited on-board resources. In this chapter, the three most common types of actuators will be examined from the perspective of their utility for miniature robotic
systems.
4.1
Piezoelectric Actuators
The word “piezo” is derived from the Greek word for pressure. Discovered in 1880 by Jacques and
Pierre Curie, piezoelectric materials create an electric charge when under mechanical stress. A very
common natural piezoelectric material is quartz. Initial experiments with quartz showed that an
electric field applied across the crystal would generate mechanical strains as well. This is called
the inverse piezoelectric effect and is the working principle of all piezoelectric actuators.
Special ceramics that exhibit the piezoelectric effect to a greater extend compared to natural materials have been developed. Perhaps the most common of these special ceramics is Lead Zirconate
Titanate (PZT). Raw PZT material is treated in a process called poling to induce piezoelectric properties [19]. In this process, PZT ceramic is heated and a strong electric field is applied (>2000
V/mm). This creates groups of dipoles with parallel orientation called Weiss domains. This alignment is roughly maintained after cooling and the ceramic is poled. After poling, when an electric
field is applied across the ceramic, the Weiss domains increase their alignment proportional to the
applied voltage. With the increased alignment, the ceramic expands in the direction of the applied
13
field and contracts in the perpendicular axes. This creates a linear actuator. Figure 9 illustrates the
poling process.
-
- --
-
+
-
-
+
+
-
+
-
+
+ +
-
-
- -
+ ++
- - -
-
+
+ +- +
+ +
+ +
+
+
+
-
+ + +
Before Poling
--- -
During Poling
-
+
+
After Poling
+
Figure 9: Dipole orientation in PZT ceramic
The maximum electric field strength that PZT ceramics can withstand are 1 to 2 kV/mm [19]. Typical values for maximum strains achieved along the direction of applied field are around 0.1 %. One
common way to reduce the applied voltage to more practical levels is to use multilayer ceramics.
Thin layers of PZT material are stacked together and electrically connected in parallel. The maximum applied voltage is limited by the thickness of a single layer, however, displacements of individual layers are accumulated. This way low voltage PZT actuators are made with layers of 20 to
100 µm thickness operating around 100 V [19]. Figure 10 illustrates a stacked type linear PZT actuator.
Figure 10: Stacked piezoactuator
14
Piezoelectric actuators theoretically have infinite resolution. Practically, the resolution is degraded
by control electronics, noise, mechanical inaccuracies, hysteresis effects and creep. Nevertheless,
sub-nanometer resolution is achievable making them the actuator of choice in many precision applications with small displacement needs.
Actuator designs for higher displacement usually employ a mechanical leverage to increase travel.
Typically, a mechanism working close to a kinematic singularity is used for high magnification
[16]. Simple PZT actuators (i.e. without mechanical magnification) have high stiffness and have a
controlled bandwidth of several kHz. Mechanical amplification may reduce both stiffness and
bandwidth.
One distinct type of actuator using piezoelectric elements is the ultrasonic motor [16]. These types
of motors have a rotor that rests on a stator made of piezoelectric material. The stator is excited by
a voltage signal to create travelling waves and cause a rubbing movement between the stator and
the rotor. Figure 11 illustrates the working principle. Typical characteristics of these motors are
high torque at low speed and high holding torque due to friction between stator and rotor. They are
also suitable for hazardous environments since no sparks are produced. The inherent high torque
at low speed eliminates the need for a complex gear box in many cases.
Rotor
Stator
Travelling Wave
Figure 11: Ultrasonic Motor
Typically, the ultrasonic motor is excited by two sine waves 90 ° out of phase with an amplitude
more than 100 V. Although the motor can be constructed in a small size, the necessary electronics
to generate the drive signals is quite complex.
As examined above, despite their high resolution and high bandwidth advantages, piezoelectric actuators are not the best match for meso-scale mobile robots that work on battery power and have
limited volume for electronics.
4.2
15
Shape Memory Alloy (SMA) Actuators
Shape memory alloys are metallic materials with a unique ability to change their shape at their
transformation temperature. When the metal is deformed and then heated above this temperature
it recovers its original shape and is able to exert a large force during recovery. This property has
been utilized in many different ways including actuation.
Shape Memory effect was first observed in a AuCd component in 1932 [9] and in a CuZn component in 1938. However, it was not until 1962 that the effect was discovered in nickel-titanium (NiTi) alloys which are the most common type of shape memory alloy on the market today.
The basis for the shape memory effect is the phase transformation that the crystal structure of the
alloy exhibits when its temperature goes above or below its transformation temperature. Below the
transformation temperature the alloy is in a soft martensite phase and can be deformed up to approximately 8 percent. Above the transformation temperature it changes in to a stronger austinite
phase. The soft martensite phase can be deformed easily and upon transformation to the austinite
phase the material recovers the undeformed shape. In fact, the transformation does not occur at a
certain temperature but within a narrow range. Additionally, it shows hysteresis behavior such that
the transformation from martensite to austinite does not occur at the same temperature range as the
austinite to martensite transformation. Figure 12 illustrates the shape memory effect with hysteresis. The beginning and the end of martensite to austinite transformation is shown as Ms and Mf
respectively. Similarly As and Af are for austinite to martensite transformation. Table 3 lists some
important properties of NiTi shape memory alloys.
Table 3: NiTi Alloy Properties
Density [g/cm3]
6.45
Resistivity, Austinite [microohms.cm]
100
Resistivity, Martensite [microohms.cm]
70
Thermal Conductivity, Austinite
[W/cm. ° C]
18
Thermal Conductivity, Martensite
[W/cm. ° C]
8.5
Young’s Modulus, Austinite [GPa]
83
Young’s Modulus, Martensite [GPa]
28 to 41
Ultimate Tensile Strength [MPa]
895
Transformation Temperatures [ ° C]
-200 to110
Maximum shape memory strain [%]
8.5
16
Figure 12: Shape Memory Effect
The alloy composition effects these values. NiTi alloys have 51% nickel and 49% Titanium. Excess nickel strongly depresses the transformation temperature and increases the yield strength of
the austinite. Other frequently used elements are iron and chromium (to lower the transformation
temperature), and copper (to decrease the hysteresis and lower the deformation stress of the martensite) [9]. The transformation temperature of NiTi alloys can be adjusted from over 100 ° C to cyrogenic temperatures.
Actuators using the shape memory effect are mostly linear. Usually, SMA material in wire form is
strained by a bias force exerted by a spring or deadweight. Upon heating, which is almost always
done by passing current through the wire, shape recovery occurs. Stresses of 170 MPa or more may
be exerted during recovery. Initial strain is usually limited to 4% to avoid reduction in shape recovery after many cycles. Tens of millions of cycles are possible at low strain [1].
The thermal nature of shape memory effect limits the bandwidth of actuators utilizing this phenomena. Heating the alloy with current is relatively fast however the cooling phase, which is usually
unforced, is slow. Actuators capable of 4 cycles per second have been reported that use SMA wire
both for actuation and bias force [6]. However, faster cooling times are expected as the actuator
size decreases since heat capacitance decreases faster than surface radiation (volume versus surface
area) with miniaturization.
17
The biggest advantage of SMA based actuators is their simplicity and thus reliability. Also the
power to weight ratio is high. However, they are usually on-off type actuators without position control.
4.3
Electro-Mechanical Actuators
Electro-mechanical actuators, or electric motors, dominate the world of actuators in most fields.
Electrical energy is easy to transmit and the well established network of power lines providing energy everywhere at relatively low cost is one reason for their popularity. Over the years, various
types of electric motors have been developed that address the specific needs of different application
areas. Figure 13 shows basic types of electric motors.
Figure 13: Electric Motor Types
All electric motors convert electrical energy to magnetic and then to mechanical. Although linear
motors which produce force along a line are common, the most abundant type is the torque motor
with a rotating shaft.
The two major categories of electric motors are AC and DC motors. DC motors do not actually
operate with direct current but they commutate the dc current supply at their terminals either electronically (as in brushless dc motors) or mechanically (brushed motors) to generate a rotating magnetic field.
18
DC motors are widely used in robotic and other servoing applications due to the ease of their control. For mobile applications running on battery power, they are practically the only choice. The
most common type is the brush type dc motor. The operating principle of a brush type dc motor is
illustrated in Figure 14. A constant magnetic field B is supplied either by an electromagnet
(wound-field) or a permanent magnet and current I passing perpendicular to this field runs through
the coil with length L . A net force F is generated on each side of the coil perpendicular to B and
I defined by the Lorentz Law:
F = I⋅L×B
The resulting torque will rotate the coils 90 degrees around the motor axis O′ – O to the equilibrium position. In a real motor there are more than one set of coils (usually each having more than
one turns) that have an angular displacement around the motor axis. Using mechanical or electronic
commutation, these coils are energized in order to ensure constant rotational motion. Figure 15 illustrates a typical dc motor with a pair of permanent magnets used to supply the constant magnetic
field. One set of coil windings is shown terminating at the commutator. A pair of brushes (not
shown) will conduct the current through the coils in the correct order as the commutator rotates
with the shaft.
I
F
B
F
Figure 14: DC motor operation
From this basic definition of a dc motor one can see the produced torque (force multiplied by radius) is proportional to both the diameter and length of the motor. In fact, as the diameter increases,
the circumference increases as well enabling the designer to place more coils. Therefore, a general
relationship between motor size and torque capabilities can be stated as [8] :
T = k⋅D ⋅L
2
19
COMMUTATOR
Figure 15: Permanent magnet motor
In this equation k is a constant dependent on design parameters other than size such as magnetic
field source (i.e. electro-magnet or permanent magnet), brushes and bearings. D and L are armature diameter and length respectively.
It is apparent that miniaturization of the electric motor is not advantageous for its performance
since the torque output decreases by the cube of the linear dimension. Still, with efficient design
and new permanent magnets with high magnetic field density, dc electric motors at very small sizes
are feasible. The introduction of Alnico and Ferrite magnets in early 1940’s, and the discovery of
rare-earth magnets in 1960’s enabled motor manufacturers to decrease the size of pm motors while
increasing power capacity and decreasing costs [15]. Although the relative scarcity of some of the
rare-earth magnetic materials (particularly cobalt and samarium) puts some practical limits on their
use in commercial applications, development of NdFeB (Neodymium-Iron-Boron) magnets promises a source of high energy magnetic materials with relatively plentiful sources [15].
Brush type permanent magnet electric motors can be found down to 8 mm in diameter on the market (i.e. MicroMo series 0816 motors). However, as the size (and thus torque output) decreases, the
effect of frictional losses at the brushed commutator increases. Brushless motors, therefore, have
the advantage of increased efficiency over brushed ones at all sizes. It is for this reason that the
smallest motors (5 to 1.9 mm diameter) on the market are all brushless.
Brushless dc motors can be described as an “inside out” version of typical brushed motors. The
permanent magnets are on the rotor and the coils are stationary on the stator. A typical three phase
brushless dc motor is illustrated in Figure 16. Three pairs of windings are positioned at 120 degree
20
intervals around the rotor. When they are excited in the correct order, a rotating magnetic field is
generated and the permanent magnet rotor rotates to align itself with this field.
M rotor
Stator windings
Figure 16: Brushless Motor
For correct excitation of the coils, the rotor position is fed back to the driver electronics. Usually,
an optical or hall-effect based encoder that is attached to the back of the motor shaft is used. Another method is based on back emf generated by the rotating magnet. The coils are energized such
that only two coils are active at a time, leaving the third open for sensing of the back emf generated.
With all methods, current or voltage control is possible in the same way as a brush type motor.
An added advantage of brushless operation is the avoidance of spark generation at the commutator.
Other than decreasing the operating life of the motor, these sparks generate high electro-magnetic
interference (EMI) and can also be dangerous in certain hazardous environments.
The efficiency of an electric motor is the ratio of mechanical power output to electrical power input. At steady state this relationship can be stated as
T⋅ω
ε m = ----------- [%]
V⋅I
Where T [N-m] is output torque, ω [rad/sec] is shaft speed, V [Volts] is the dc voltage across the
terminals and I [Amps] is current through the motor. Small sized commercial motors typically
have 60-80 % maximum efficiency. Most small dc motors produce power at high speeds and low
torque. A gear box is often used to produce more torque at lower speeds at the expense of decreased
overall efficiency due to additional frictional losses. However, high quality gearboxes can operate
around 90% efficiency [15].
21
As mentioned before, dc motors are relatively easy to control compared to other types. There is a
linear relationship between motor torque (load) and speed at a fixed voltage. As the load increases
the speed decreases linearly. Similarly, voltage is related linearly to speed at a constant load. Usually, motor characteristics are shown in torque-speed plots at the nominal operating voltage for
which the motor is designed. Figure 17 illustrates such a plot with power output (torque x speed)
and efficiency also plotted. As seen from the graph, maximum efficiency of energy conversion occurs at about 10% of the stall (zero speed) torque, which is typical for most motors. However, the
maximum power output occurs midway through the speed-torque graph.
Figure 17: DC motor torque-speed curve
22
5. DESIGN WITH MINIATURE ACTUATORS
As an application of miniature actuators, design of the video reconnaissance module is presented
in this chapter. The required properties of the system are defined and alternative designs are developed. Issues in mechanical design with miniature actuators and their control electronics are examined.
5.1
Specifications for the Video Reconnaissance Module
As explained in Section 2.3 the video reconnaissance module will transmit live video through a
wireless link back to the operator. It is required that the video camera provide a complete view of
its surroundings. Additionally, the video reconnaissance module can not protrude from the tubular
shell since this would interfere with the launch and mobility of the robot.
There is very limited volume available on the robot for sensors and other payload. The shape and
dimensions of the payload volume is illustrated in Figure 18. The specifications of the video camera are summarized in Table 1 in Section 3.1. Figure 19 illustrates its shape and size.
Figure 18: Scout payload volume
5.2
Alternative Designs
A trivial design for the video reconnaissance module with minimum complexity is a static camera
fixed inside the robot. The camera sees through the transparent shell of the robot and depends on
the robots mobility for visually tracking the surrounding environment. A static camera has been
placed on some of the first series of prototype scouts in order to test this concept. The robot can
adjust the pan and tilt angle of the camera by rotating itself on the ground (through independent
0.393 ”
23
0.59 ”
0.236 ”
0.59 ”
Figure 19: Video camera
tilt
Winch wire
camera
transparent shell
Spring arm
Figure 20: Static camera position and tilt control by spring arm
control of its wheels) and rolling along its longitudinal axis by winching in and out the spring arm.
Figure 20 illustrates the position of the camera on the robot and control of the tilt axis of the camera
by the winch mechanism.
Despite the simplicity this offers, the static camera design displayed some problems during tests.
The pan axis, which is controlled by rotating the robot on the ground, is not smooth and is effected
by ground conditions and obstacles. The spring arm winch mechanism that uses an electric motor
coupled to a gearbox operates very slowly and is the most power consuming subsystem (~1.7 W)
on the robot.
An alternative design was sought that would provide pan-tilt capability independent of the robot.
However, the limited payload volume prohibits the camera from being panned and tilted complete-
24
ly inside the robot. Therefore, in addition to pan and tilt action, a third component of motion to
raise the camera outside the shell and retract it is needed. The first generation video reconnaissance module (VRM-1) was designed and built to meet these specifications.
5.3
First Generation Video Reconnaissance Module (VRM-1)
5.3.1 Mechanical construction
With the specifications on the video camera and payload volume fixed, a mechanical system to add
pan, tilt and pop-out/retract capabilities to the camera was designed.
A significant design challenge is to choose an actuation technology that will enable the system to
fit inside the small payload volume. Three common types of actuators were previously analyzed in
more detail. Table 4 summarizes the advantages and disadvantages of these actuation technologies.
Table 4: Comparative Summary of Actuators
Actuator
Advantages
Disadvantages
Shape
Memory
Alloy
•
•
•
Simple and robust
High actuation force
Simple two-state (on/off)
action
•
•
•
•
Low strain (% 4)
Low bandwidth (<1 Hz)
Inefficient with batteries
Requires complex mechanism
for position control
PiezoElectric
•
High torque at low speed and
high holding torque at zero
power (ultrasonic motor)
High bandwidth (kHz)
•
High voltage and special
waveform required.
Linear types have low strain
(% 0.1)
Many different size and types
available on market
Easy to use, reliable
Easy to control
•
•
ElectroMagnetic
•
•
•
•
Requires gearbox and some
electronics
The electro-magnetic actuator was chosen as the primary actuator for the video reconnaissance
module due to its availability, ease of use and control. Table 5 lists miniature motors available on
the market. Among these actuators only two, the 3 mm diameter Smoovy and the 1.9 mm diameter
Micro-Mo motor are small enough to fit inside the payload volume. The Micro-Mo motor has some
disadvantages compared to the Smoovy. It is designed for operation with 1 V nominal voltage so
a dc-dc voltage conversion from the circuit supply voltage (5 V) is necessary. Additionally, the
much greater reduction ratio gearbox on the Smoovy provides high holding torque (without power)
for locking the mechanism at intermediate positions.
25
Table 5: Miniature Motors
Model
Type
Diameter x Length [mm]
Micro-Mo 0816
•
Brushed
•
8 x 25 (without gearbox)
Maxon 199608
•
Brushless with hall
effect sensors
•
6 x 21 (without gearbox)
Micro-Mo 0206
•
Brushless, sensorless
•
1.9 x 9.1 (with 47:1
gearbox)
Smoovy
•
Brushless, sensorless
•
3 x 15 (with 125:1
gearbox)
Hence the Smoovy motor was chosen as the actuator for the video reconnaissance module. It is a
brushless, sensorless motor with an integrated gearbox of 125:1 reduction. The output shaft is 1mm
in diameter and is supported by bearings. Table 6 lists some of its specifications and Figure 21 illustrates the motor.
Figure 21: Smoovy motor
The motor has a small, integrated high reduction ratio gearbox and is an feature making this motor
more appropriate than others investigated. A planetary gear design is used with 3 stages, each pro-
26
Table 6: Smoovy Motor Specifications
Size
•
OD 3mm length 15 mm
including 125:1 gearbox
Nominal Voltage
•
4V
Max. continuous torque
output
•
2.2 mN-m
Weight
•
<1 gr
Gearhead efficiency
•
%60
Figure 22: Motor-gearbox assembly
viding a 5:1 reduction in speed. The gear modulus (Pitch diameter/number of teeth) is 50 µm. Figure 22 illustrates the gearbox-motor assembly.
Manufacture of a gearbox of this size is a challenge in itself. The gears on the Smoovy motor are
0.6 to 1 mm in diameter and are made of steel using traditional machining techniques used in Swiss
watchmaking industry. For smaller gears, however, neither traditional methods nor the common
EDM techniques can provide the required manufacturing tolerances. The planetary gears on the 1.9
mm Micro-Mo motor, for example, have an outer diameter of 180 µm and tooth-face width of 300
µm. These gears are made of polymer material, injection molded into molds made using the LIGA
technique. LIGA is a German acronym for a micromachining process that involves lithography,
electrodeposition and molding. This method uses extremely well-collimated x-rays generated by a
synchrotron to create high aspect ratio (at least 100:1) parts, usually made of nickel. The x-rays are
used to create an electroplating template into which nickel is deposited. Typically, the resulting
metallic structure is the end product itself. Gears made with this technique were used on earlier versions of the Smoovy motors, however, LIGA gears were replaced by traditionally machined steel
gears due to their longer operational life. Micro-Mo gears, on the other hand, use the LIGA technique to create the mold cavities for injection molding the polymer gears. Although the aspect ratio
27
is not large (180:360), LIGA provides smooth wall surfaces and close tolerances that also aid in
taking the parts out of the mold.
Figure 23 illustrates the pan/tilt/pop-up mechanism of VRM-1. Three miniature electric motors are
used. The tilt motor is directly coupled to the camera, and the pan motor carries the camera and the
tilt motor. A third motor is coupled to a leadscrew that is used to operate a four-bar mechanism.
The parallel four-bar mechanism moves the camera and the other two motors up and down. Figure
24 shows the up (deployed), tilted and panned states. Before deployment, the robot rolls itself ~150
degrees to face the camera side up (see Figure 20) Note that the first 90 degrees of tilt is necessary
to clear the camera out of the shell. The remaining 90 degrees of tilt rotation, together with the 180
degree pan, provides a hemispherical field of view.
5.3.2 Driver electronics
As mentioned earlier, the Smoovy motor is a brushless dc design that has no sensors for feedback.
A common method used for commutation of sensorless motors is back-emf sensing. In this method,
the motor phases (windings) are commutated such that one phase is left energized at each commutation cycle. Due to the rotation of the magnetic rotor, a sinusoidal voltage is induced across this
coil (back emf). This voltage is tracked by driver circuitry for its zero crossing and motor windings
are energized in correct timing [18]. A commercial integrated circuit is available that combines
back-emf sensing and driving circuitry in a single package [18]. However, back emf produced by
the small windings of the Smoovy motor is not sufficient for correct operation of this circuit [21].
Therefore only choice left is sensorless, open loop commutation. In this mode, the motor is used
essentially as an AC synchronous motor. In AC synchronous motors, the three phase voltage is applied to the windings to create a magnetic field that rotates at the supply frequency. The rotor follows this field at the same frequency and displays a constant speed-torque characteristic within its
load range. The speed drops abruptly when the maximum load capability is exceeded since the rotor is no longer synchronous with the field. Figure 25 shows the flat speed-torque characteristic of
the 3 mm Smoovy motor (without the gearbox) when driven with 3 phase ac supply [20].
A commercial driver circuit supplied by the Smoovy company (model CPS00002) was used for
driving the motors on VRM-1 [22]. This microprocessor controlled circuit creates square wave patterns that approximate the 3 phase (120 degree) sinusoidal voltage supply to an AC motor. The frequency is adjustable for speed control. The circuit board is 20x30x5 mm in size. Figure 26 shows
a simplified driver schematic and phase voltages during a single cycle.
When square waves are used instead of sinusoids, torque ripple may be observed, especially at low
speeds [12]. However, generating sinusoidal waves at variable frequency requires more complex
circuitry compared to the simple on/off operation of a square wave driver. An alternative method
is pulse width modulation (PWM) where the duty cycle (time-on/total cycle time) of the square
wave is changed. This results in an average voltage proportional to the duty cycle, and, thus, volt-
28
Figure 23: VRM-1
Figure 24: VRM-1 in up, tilted and
panned configuration
29
30
Figure 25: Smoovy motor speed-torque characteristic
Figure 26: Square wave 3 phase driver
ages between the supply voltage (typically 5 V) and ground can be approximated. Owing to the
inductance in coils, currents in the windings vary more smoothly than the voltages. Figure 27
shows the PWM waveform. Using this technique, trapezoid waves (for a better approximation of
a sinusoid than a square wave) and sinusoid waves can be generated. Although the same, driving
circuit, is used, the microcontroller code for generation of these patterns become more complex..
The driver circuit was multiplexed between the three motors of the VRM-1 using a custom designed circuit that relays the phase voltages to individual motors depending on a 2 bit address selected by the scout’s main computer. The circuit uses nine surface mount (SO-8) MOSFET relays
with a 2 bit address decoder and has a size of 26x32x4 mm. Figure 28 shows the system diagram.
Von
31
Ton
Vaverage
Ground
Tcycle
T on
-----------⋅V
V average =
T cyle on
Figure 27: Pulse Width Modulation
Wireless Video Transmitter
Scout Computer
Video Camera and
Pan-Tilt Mechanism
Motor Driver and
Multiplexer
Figure 28: System diagram for scout-VRM interface
5.3.3 Test results
The VRM-1 was interfaced to a scout for testing. The driver circuit was set at a fixed speed while
the command structure for each actuator (sent from the operator to the robot) contained the direction and time duration of operation. The two address bits on the multiplexing circuit were selected
by the scout computer for one of three actuators and an off (stop) state where none of the actuators
is energized.
Each motor consumes about 70 mA of current at 5 V. Although the basic mechanism operates as
expected a number of problems were observed with the overall design.
The main problem is related to the fact that mechanical construction of the miniature motors is different from other small and medium sized ones. Typically, motor housings with integrated gearboxes are made of metal and the output-shaft is directly coupled to the load. In other words, the
32
gearbox design is made to account for increased load at the output shaft. In case of the Smoovy
motor, the gearbox housing is made of a plastic material and is lightly fitted on the motor housing
(see Figure 22). Although the gearbox is specified to carry 40 N axial and 25 N radial load [23] the
housing is not able to take any axial load without dismounting from the motor. This was observed
even during careful assembly of the mechanism. However, the VRM-1 design does not have any
means for protecting the motors from radial and axial loads since their specifications were far beyond anticipated loads on them.
A similar problem exists with the tube shaped plastic housing that also has internal threads to serve
as the outer ring gear for the planetary gear system. Since the motor has no special means for
mounting (i.e. bolt threads at the faceplate), a tight fit mount was used for VRM-1. However, the
flexible outer housing squeezes the planetary gears and inteferes with the operation of the gearbox.
The large size of the driver electronics was another problem with the VRM-1. In fact, the spring
arm winder mechanism on the prototype scout used for VRM-1 testing was removed to allow space
for driver circuitry. Since small sized surface mount components were already used, the only way
to reduce the size of electronics is to reduce the number of motors. This requires coupling of different motions (i.e. tilt and deploy/retract) or mechanical multiplexing of an actuator between one
or more actions.
The fixed operation speed of the driver circuit was found to be inadequate for control of different
actuators. For example, the deploy/retract motor uses a screw drive with a large reduction in speed
while others are directly coupled. The driver speed was set at a value suitable for directly coupled
actuators (pan and tilt) which resulted in very slow action of the four-bar mechanism.
5.4
Second Generation Video Reconnaissance Module (VRM-2)
The experience gained from VRM-1 was used in the design of second generation video reconnaissance module to solve the problems encountered with the first one.
5.4.1 Mechanical design
A design with a reduced number of actuators, and thus electronics, was sought for VRM-2. Additionally, all motors were coupled by bearings for protection from axial loads.
A lead screw based dual action mechanism was developed that uses only one motor for independent pan and up-down motion. Figure 29 illustrates the operation principle of this mechanism. A
lead screw and nut assembly is coupled to an actuator that rotates the nut. The leadscrew rotates
with the nut due to friction between them. If, however, the leadscrew is prevented from rotational
motion but not axial, then it moves in the axial direction.
Lead screw follows nut
when free to rotate
33
Lead screw moves in axial direction
when prevented from rotation
Figure 29: Dual action mechanism operation
The dual action mechanism of VRM-2, illustrated in Figure 30, works as follows. The lead screw
is inserted through a gear-nut assembly which is actuated by a meshing gear coupled to the motor.
A collar, free to rotate around the nut also hosts a pin which is fixed to the lead screw at the top.
The pin is free to move axially inside the collar. When the nut rotates, the leadscrew, pin and the
collar rotate together with the nut. When the collar is clutched and prevented from rotation, the
leadscrew and the pin move axially as the nut rotates. The clutching mechanism on the collar is
friction based and is actuated by shape memory alloy wire. Figure 31 illustrates the complete mechanism. The two clutch arms are made of spring steel and are connected with an SMA wire. When
the wire is heated by passing current through it, it contracts and forces the spring arms onto the
collar. A high friction material is placed on both the collar and the arms to ensure effective clutching.
The lead screw carries the tilt motor assembly up and down and rotates it for panning. This way
two independent actions are controlled by a single motor. The tilt motor is directly coupled to the
camera, however, a bearing protects the motor shaft from axial loads. Figure 32 displays VRM-2
in up, tilted and panned positions. VRM-2 proved to be much more successful in operation as compared to VRM-1. Tests were performed to demonstrate its dynamic capabilities using computer vision techniques, and are described in the next chapter.
34
Lead-screw
Pin
Collar
Bearing
motor and gear
Figure 30: VRM-2 cross-section
Gear-nut
camera
pan motor
collar
tilt motor
housing
Clutch arms
SMA wire
Figure 31: VRM-2
Figure 32: VRM-2 up, tilted and panned
35
36
6. Active Vision with the Video Module
It is essential for a distributed robotic system to exhibit some degree of autonomous behavior, especially when the number of robots is high compared to the number of operators. Although the basic functionality of the video reconnaissance module is to increase the effective field of view of the
camera, it is also desirable to implement computer vision techniques to aid in autonomous and
semi-autonomous surveillance.
Active vision is a field of computer vision in which the camera is able to move under control, as in
the case of the pan-tilt video reconnaissance module. Some of the common application areas of active vision are robotic systems for object manipulation, mobile systems and surveillance. Implementing active vision techniques with a system like the VRM that has miniature actuators and a
unique structure presents its own challenges. This chapter discusses these challenges and presents
an implementation of autonomous motion detection and tracking with the VRM.
6.1
Experimental set-up
The onboard processor on the scout does not have image processing capability at this stage of the
project, so all processing is done on a host computer that receives live video and returns motion
commands to the scout. However, all tracking and motion detection software was developed anticipating implementation on a small embedded processor with limited speed and memory.
Microcontroller
Video reconnaissance module
Figure 33: Experimental setup
37
The experimental setup is shown in Figure 33. A single board microcontroller system that uses a
Motorola 68HC11 processor is interfaced to the VRM to emulate the scout and control the actuators. A host PC that runs a 400 MHz Intel Celeron processor with the Windows ‘98 operating system is connected to the video camera through a Sensoray 611 video digitizer card. Live video is
processed on the PC in real-time (30 frames/sec), and motor speed commands are send to the microcontroller through an RS-232 serial connection which then commutates the motors accordingly.
Visual Tracking with the VRM
A program that applies computer vision techniques was developed in C for the host PC. The program was written as a testbed for different algorithms and interacts with the user through a GUI. A
tracking module was written to demonstrate visual tracking with the VRM and to evaluate its dynamic capabilities.
A correlation based method called the Sum-of-Squared-Differences Optical Flow (SSD) was implemented for tracking selected features in real-time. The basic assumption of SSD tracking is that
intensity patterns I ( x, y, t ) in a sequence of images (i.e. video) do not change rapidly between successive images I ( x, y, t + 1 ) . For example, if an image feature is located at P1 ( x 1, y 1, t ) then its
position in the next image will be displaced within an MxM boundary of
P 2 ( x 1 + dx, y 1 + dy, t + 1 ) , ( dx, dy < M ). To implement the algorithm, a T NxN template around the
feature to be tracked is first acquired. An SSD correlation measure (error function) is calculated for
each possible displacement (dx,dy) within a W MxM search window in the new image I ( x, y, t + 1 )
as:
SSD ( dx, dy ) =
∑ [ I( x
1
+ dx + i, y 1 + dy + j ) – T ( x 1 + dx + i, y 1 + dy + j ) ]
2
i, j ∈ N
The distance (dx,dy) that has the minimum SSD measure is assumed as the displacement of the
feature. Figure 34 shows typical SSD error plot for an edge feature. The surface generated by the
SSD error function above an MxM neighborhood of the feature has a minima at the displaced position of the feature where the best match with the template occurs.
SSD tracking is an effective method as long as the basic requirement that image patterns do not
change considerably between successive images is satisfied. It is desirable to select features with
high gradients, such as edges and corners, that are distinct from their neighboring pixels. The
amount of processing is highly dependent on the template size N and search window size M . A
large template will increase robustness, while a larger search window will handle larger displacements, provided frames can be processed in real-time. The described system can simultaneously
38
Image
SSD error surface for an edge
Template obtained from the image
Figure 34: SSD error surface
track 5 features each with a 12x12 template over 36x36 search window at full frame rate (30
frames/sec).
A variation to the classical algorithm was made so that a linear approximation for the position of
the feature is calculated using data from previous frames and the search window is placed to center
this anticipated position. This way features moving in relatively uniform manner can be tracked
even when their displacement is larger than the search window size.
Figure 35 shows a simplified pinhole model of the camera lens system. The center of the lens system is coincident with the world coordinate frame origin O. The image plane is the camera sensor
plane parallel to the X – Y plane at a distance f , where f is the focal distance of the system. A point
P with coordinates ( X, Y, Z ) is projected to point p with image coordinates ( x, y, z ) ) as
–X ⋅ f
x = ------------Z
,
–Y ⋅ f
y = -----------Z
(6.1)
The main objective of visual tracking is to position a point in the image (e.g. the centroid of the
tracked feature) at the center of the image plane by panning and tilting. The pan and tilt rotations
y – axis
Image Plane
39
P ( X, Y, Z )
x – axis
Y
f
Z
X
O
y
Z – axis
x
X – axis
Y – axis
Figure 35: Pinhole camera model
are related to rotations around Y and X axes respectively. The transformation matrix that defines
these rotations can be written as
T ( φ, θ ) = R ( Y, φ ) ⋅ R ( X, θ )
cos ( φ )
0
T ( φ, θ ) =
– sin ( φ )
0
0 sin ( φ )
1
0
0 cos ( φ )
0
0
1
0
0
0
0 ⋅ 0 cos ( θ ) – sin ( θ )
0
0 sin ( θ ) cos ( θ )
0
0
0
1
0
0
0
1
The relation between the angles φ and θ that will position the projection of an arbitrary point
P ( X, Y, Z ) at the center of the image plane (i.e. X = Y = 0 , can be written as:
0
0 = T ( φ, θ ) –1 ⋅
Z′
1
=
X
Y = T ( φ, θ ) – 1 ⋅ Z
--- ⋅
f
Z
1
cos ( φ )
0
– sin ( φ )
sin ( φ ) ⋅ sin ( θ ) cos ( θ ) cos ( φ ) ⋅ sin ( θ )
sin ( φ ) ⋅ cos ( θ ) – sin ( θ ) cos ( φ ) ⋅ cos ( θ )
0
0
0
x
y
f
1
0
0 ⋅Z
--- ⋅
0 f
1
x
y
f
1
(6.2)
40
Solving (6.2)
x cos ( φ ) – f sin ( φ ) = 0
x sin ( φ ) sin ( θ ) + y cos ( θ ) + f cos ( φ )sin ( θ ) = 0
x
φ = atan  --
f
(6.3)
–y
θ = atan  --------------------------------------------
 x sin ( φ ) + f cos ( φ )
From (6.3) it is clear that the pan and tilt angles are only a function of the known and measurable
variables x , y and f . Additionally, the y coordinate of an image point is effected by both pan and
tilt whereas the x coordinate is controlled only by the pan angle φ .
All visual servoing experiments were performed with the control of the pan angle only, the tilt angle being fixed. Therefore, the aim of the controller was to the fixate the x coordinate of the target
point at zero.
The relation between x and φ can be rewritten as
sx ⋅ px
x
tan ( φ ) = -- = -----------f
f
(6.4)
where s x [mm/pixel] is the physical distance in the x direction (width) per each individual pixel
on the sensor plane and p x is the x coordinate in pixels, which is the actual measured variable. For
the case where φ is small (i.e. when the tracking system is closely following the target), the relation
between pan speed (rad/sec) and image velocity can be approximated as
φ ≈ 0 ⇒ tan ( φ ) ≈ φ
·
·
φ = K ⋅ px
(6.5)
The constant K has been experimentally determined to be 0.00145 for the VRM camera. A proportional controller was used to generate the speed commands for the motor and the gain was adjusted manually. A target feature to be tracked is visually selected by the user, and its grayscale
pattern is stored in a template. The position error is calculated at every frame and a corresponding
8 bit speed value is send to the microcontroller through the serial interface. The commanded speed
of the actuator ranges between 0.05 and 1.32 rps at 0.01 rps intervals in both directions. The lower
bound on commanded speed was experimentally found to be the lowest value that the actuator rotates without significant torque ripple. Figure 36 shows the closed loop controller diagram. Figure
37 shows the step response of the controller while tracking a stationary target.
PC
Controller
Motor
41
Camera
Figure 36:Controller system diagram
Figure 37: Step response of VRM
The frequency response of the system was also experimentally determined by tracking visual sinusoidal inputs. A computer monitor was used to generate two square shaped targets, one static on
the screen and the other moving in the horizontal direction at a controlled amplitude and frequency.
The vision system constantly tracks the positions of both targets (in the acquired images), however,
the camera actively follows the moving target in order to fix it at the center of the image. The difference between the x-positions of the targets is the controlled visual input to the system (a sinusoid
at particular frequency and amplitude). The input is fed to a proportional controller to generate
speed commands for the motor. Since the camera is fixating on the moving target, the x-position
of the static target is the output of the system. For example, if the system perfectly tracks the moving target, its x-position will be zero at all images (i.e. at the center of the image) and the position
difference between the two targets will be equal to the input sinusoid. As the input frequency is
increased the system exhibits attenuation, and, thus, the output amplitude is smaller than the input.
42
Figure 38 illustrates the two square targets generated on the computer monitor. Figure 39 shows
the input and output of the system at two different frequencies. The system exhibits -3 dB attenuation at approximately 0.5 Hz. The frequency response of the system is plotted in Figure 40.
Static target
y
x
Moving target
Figure 38: Visual targets
Input
Output
Figure 39: System response to sinusoidal inputs
6.2
Motion Detection and Tracking
The SSD tracking method is not robust to changes in the tracked feature’s grayscale pattern that
may result from changes in illumination and occlusion, rotation or deflection of the feature. Additionally, for autonomous tracking an automatic feature selection procedure must be performed to
initiate tracking and update the feature template. Therefore, it may be concluded that SSD is useful
43
Figure 40: Frequency response
for tracking in structured environments where the feature changes little. One example is visual servoing of micro-manipulation instruments under a microscope. The two dimensional nature of the
problem, together with the predictable environment and controlled illumination, makes SSD a robust method of tracking under these circumstances.
For three dimensional problems there are two basic approaches to tracking. The first approach relies on object-recognition for detection of features in successive images. Some information about
the object to be recognized must be known a priori in order to make it possible to extract the three
dimensional position and orientation of the feature. However, the computational costs of object
recognition can be excessive, and special hardware is required to perform real-time tracking for
this case. Additionally, it is not always possible to have a good model of the feature when operating
in an unknown environment.
The second method of motion tracking is based on motion detection. This method does not require
any model of the feature and relies entirely on detection of motion in the image. Objects of any size
and shape can be tracked, however, additional logic is needed to discriminate between motion that
is of interest and motion that is not of interest. The method is inherently robust against changes in
feature pattern and partial occlusion.
44
Two basic approaches to motion detection are optical flow [24] and motion-energy [17] methods.
Optical flow methods, under which the SSD algorithm can be classified, find the velocity field in
the image by solving the equation:
∂f
∂f
∂f
⋅u+
⋅v+
= 0
∂x
∂x
∂t
dx
dy
where f ( x, y, t ) is the image function, u ( x, y ) =
and v ( x, y ) =
are velocity functions that
dt
dt
are being sought. Since there are two unknowns but one equation, additional constraints such as
smoothness are needed for a unique solution. Once the velocity field is found, regions of motion
are extracted and tracked. Motion-energy methods, on the other hand, find the temporal derivative
of the entire image and threshold it to extract regions of motion. The temporal derivative is usually
found by simple image substraction
( x, y, t ) – f ( x, y, t + ∇t )d f ( x, y, t ) ≈ f--------------------------------------------------------∇t
dt
This information is usually imprecise and subject to noise. Adding spatial gradient information,
such as incorporating edge strength as a multiplier before thresholding, is a frequently applied
method of improvement [17]. However, these additions increase complexity and computational
costs.
With all methods of motion detection and tracking, an additional problem is introduced with the
active camera. Egomotion, or the induced motion in the image due to the motion of the camera itself, must be discriminated from actual motion in the view. Optical flow methods of motion detection use some form of smoothness constraint to solve this problem. Once the gross velocity field
of the image, which represents the egomotion, is found, pixels that belong to the same feature but
are displaced between consecutive images due to egomotion are re-mapped with a process called
registration. When the registered images are compared (i.e. subtracted), regions of actual motion
are extracted. Again, this method is subject to noise just as with simple image substraction.
Another method of registration uses position sensor feedback (i.e. encoders on the pan-tilt camera
axes) to cancel the effects of camera motion. The method relies on the accuracy of the sensory data
and good calibration of the camera [19].
A method of motion detection and tracking that is suitable for the mechanical and anticipated computational capabilities of the VRM is needed. One application of such an algorithm would be to use
the scout in an autonomous “watchdog” mode where it continuously monitors its environment with
the video camera, detects, and then tracks motion (e.g. people walking into a room where the scout
45
is located) while alerting the operator of the situation. This method has been implemented and relies on image substraction and thresholding for detection of motion, while executing a stop-lookmove behavior to eliminate egomotion. The camera comes to a full stop before processing images
to detect motion. After image substraction, the centroid of the pixels that pass a thresholding step
is found for each frame. When the centroid moves from the center of the image, the error in the xcoordinate (the distance between the centroid and the image center) is sent to a proportional controller and the camera is commanded to move towards the detected center of motion for five frames
(165 ms) in an open loop fashion. After five frames, the camera comes to a complete stop and the
process repeats.
Stop the camera
Subtract two successive
frames and threshold
Calculate c ( k ) , the centroid of the pixels
that pass thresholding
Calculate the overall center of
motion λ ( k )
λ(k) = α ⋅ c(k) + (1 – α) ⋅ λ(k – 1)
Is λ ( k ) at the center
YES
of image?
NO
Move camera to center
λ(k ))
Figure 41: Motion detection algorithm flow chart
46
Figure 41 shows the flow chart of the motion detection algorithm. One problem with this method
is the sensitivity of the calculated center of motion to noise. As a solution, a cumulative centroid
λ ( k ) that is smoothened in time is calculated as
λ( k) = α ⋅ c( k ) + ( 1 – α ) ⋅ λ( k – 1 )
where c ( k ) is the center of motion calculated from a single frame substraction. The value used for
α was experimentally determined to be 0.4. The algorithm was tested indoors and found to be able
to detect and follow people moving inside a cluttered room. Figure 42 shows a sequence of images
captured while the camera was tracking a person walking inside a room.
Figure 42: Motion tracking
47
7. Conclusion
In this report, the design of small scaled robotic systems with miniature actuators was discussed.
A pan-tilt video camera system was designed for a miniature mobile robot to comply with volume
and power restrictions on the robot.
Video sensor and wireless transmitter technologies were summarized. CMOS type video sensors
were found superior to the common CCD type for miniature applications due to their much smaller
size and power consumption. A single-chip CMOS sensor and a miniature wireless transmitter operating at 900 MHz were chosen for the video camera system.
Three main actuation technologies that are suitable for miniature applications from millimeter to
centimeter size were discussed with an emphasis on their advantages and disadvantages for mobile
systems. Shape memory alloy actuators were found useful for simple on-off type actuation without
position control due to their simplicity and robustness. However, their slow response time is an issue. Piezoelectric actuators, on the other hand, have the fastest response of all but require high voltage and more complex drive electronics when compared to other actuation technologies. The
torque output of an electromagnetic actuator was shown to decrease by the cube of the linear dimension making them unsuitable for miniaturization in general, however they were still found to
be the most suitable type for the application due to their availability, small drive electronics and
ease of control.
Miniature electric motors available on the market were compared and a 3 mm diameter brushless
PM motor with a 125:1 integrated gearbox was chosen. Alternative designs were presented and
special issues for machines that use these motors were discussed. A special mechanism to use one
motor for two independent actions were developed to reduce the number of necessary electronics.
Motor driver electronics were developed for brushless, sensorless operation of these motors.
Finally, computer vision techniques were applied to demonstrate and evaluate the capabilities of
the system. Lacking position sensors, the system relies on visual information for orientation control. Visual tracking with the SSD technique was demonstrated and system response to step and
sinusoidal inputs were plotted. Additionally, a simple motion detection and tracking algorithm
suitable for mechanical and computational capabilities of the system was developed.
Future goals for the video reconnaissance module include on-board image processing, integration
of sensory components for camera orientation feedback and development of better mechanisms for
further reduction in the number of actuators.
Miniature actuator technology is still in its early stages. As more and better actuators become available, the number of applications that employ them will increase. The absence of standard, off-theshelf drive components of comparable size is a current drawback of using miniature actuators.
Common components like gears, flexible shaft couplings or clutches are still needed in meso-scale
48
robotic systems. Further reduction of the size of drive electronics and sensors is also needed for
this field. The technology for miniaturization of drive electronics to a size comparable with miniature actuators already exists, however, more commercial applications for miniature actuators are
needed in order to push the development of this technology further.
49
8. References
[1]
J. Conrad, J. Mills, Stiquito Advanced Experiments with a Simple and Inexpensive Robot, IEEE Computer Society,1998.
[2]
A. Cox, E. Garcia, M. Goldfarb, “Actuator Development for a Flapping Microrobotic Microaerial
Vehicle”, Microrobotics and Micromanipulation, A. Sulzman and B.J. Nelson, Editors, Proceedings of SPIE Vol. 3519, pp. 102-108, 1998.
[3]
K. Deng, A. Dewa, D. Ritter, C. Bonham, H. Guckel, “Characterization of Gear Pumps Fabricated by
LIGA”, Microsystem Technologies, Vol.4, No.4, pp.163-167, 1998.
[4]
P. Denyer, “CMOS vs. CCD”, whitepaper by Vision Company, UK, 1999, “www.vvl.co.uk/whycmos/whitepaper.htm”.
[5]
M. Dornheim, “Tiny Drones May be Soldier’s New Tool”, Aviation Week & Space Technology, pp.
42-48, June 8 1998.
[6]
K. Gabriel, W. Trimmer, J. Walker, “A Micro Rotary Actuator Using Shape Memory Alloys”, Sensors & Actuators, Vol. 15,No.1, pp. 95-102, 1988.
[7]
B. Grob, Basic Television and Video Systems, McGrawHill,1999.
[8]
D. Hanselman, Brushless Permanent-magnet Motor Design, McGraw-Hill,1994
[9]
D. E. Hodgson, M. H. Wu, R. J. Biermann, “Shape Memory Alloys” , whitepaper by SMA-Inc.,
http://www.sma-inc.com/SMAPaper.html
[10] D. F. Hougen, S. Benjaafar, J. C. Bonney, J. R. Budenske, M. Dvorak, M. Gini, H. French, D. G.
Krantz, P. Y. Li, F. Malver, B. Nelson, N. Papanikolopoulos, P. E. Rybski, S. A. Stoeter, R. Voyles
and K.B. Yesin, ”A Miniature Robotic System for Reconnaissance and Surveillance”, IEEE International Conference on Robotics and Automation (ICRA2000), 2000.
[11] H. Ishihara, F. Arai, T. Fukuda, “Micro Mechatronics and Micro Actuators”, IEEE/ASME Transactions on Mechatronics, Vol. 1, No. 1, pp. 68-79, 1996.
[12] T. Kenjo, Power Electronics for the Microprocessor Age, Oxford University Press, 1990.
[13] G. Kovacs, Micromachined Transducers Handbook, McGrawHill, 1998.
[14] A. Lipton, H. Fujiyoshi, R. Patil, “Moving Target Classification and Tracking from Real-time Video”,
IEEE Workshop on Applications of Computer Vision (WACV), pp.8-14, 1998.
[15] MicroMo Company Catalog, USA, 1997.
[16] R. Moroney, R. White, R. Howe, “Ultrasonic Micromotors: Physics and Applications”, IEEE Micro
Electro Mechanical Systems An Investigation of Micro Structures, Sensors, Actuators, Machines
and Robots, IEEE, pp. 182-187, 1990.
[17] D. Murray, A. Basu, “Motion Tracking with an Active Camera”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16, No. 5, pp. 449-459, 1994.
[18] Phillips Semiconductor product data sheet, TDA5140A Brushless DC Motor Drive Circuit, April
1994.
[19] Physic Instrumente Company Catalog, “NanoPositionning”, Germany, 1999.
[20] RMB Smoovy product data sheet, SYH39001 High Precision Motor 3 mm, February 1999.
[21] RMB Smoovy product Application Notes, CCS00001 Controller, November 1998.
50
[22] RMB Smoovy product data sheet, CPS00002 Controller, November 1998.
[23] RMB Smoovy product data sheet, SPH39003 3mm Gearmotor, February 1999.
[24] G. Whitten, “Independent Motion Target Cueing for Moving Sensor Systems”, Applications of Artificial Intelligence VI, Proceedings of SPIE Vol. 937, pp. 336-341, 1988.
[25] Y. Zhou, B.J. Nelson, “Adhesion Force Modeling and Measurement for Micromanipulation”,
Microrobotics and Micromanipulation, A. Sulzman and B.J. Nelson, Editors, Proceedings of SPIE
Vol. 3519, pp. 169-180, 1998.

UMN-AML-TR-00-02 - Department of Mechanical Engineering

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