Autonomous Vehicles:

Transcription

CERMA 2007 / Tutorial
Autonomous Vehicles:
Research, Design and Implementation of
Intelligent Autonomous Vehicles
Autonomous Vehicles Research Group - GPVA
http://www.eletrica.unisinos.br/~autonom
Tutorial page: http://inf.unisinos.br/~osorio/palestras/cerma07.html
http://inf.unisinos.br/~osorio/palestras/cerma07.html
Dr. Fernando S. Osório
Dr. Christian R. Kelber
Dr. Cláudio R. Jung
M.Sc.
M.Sc. Farlei Heinen
- Applied Computing PostPost-Grad. Program PIPCA
- Electrical Engineering / Computer Eng.
- Applied Computing M.Sc.
M.Sc. Program PIPCA
- Computer Engineering B.Sc. (Director)
Grupo de Pesquisas em Veículos Autônomos
Autonomous Vehicles Research Group - Unisinos
1
Veículos Autônomos Inteligentes
•
•
Introduction
Robotic: Automatons, Mobile Robots and Autonomous Robots
•
Perception, Action, Locomotion e Communication
Control and Intelligence
Intelligent Vehicles
•
Technologies for Vehicle Automation
Control pyramid
Intelligent Control of Autonomous Vehicles
Control: Computational Architectures
•
Simulation of Autonomous Vehicles
Computer Vision
•
Practical Applications
Agenda
2
1
CONTROL:
CONTROL: Computational Architectures
=> From where do I start? Modeling and Simulation
• Models:
- Sensorial Models
- Actuator Models
- Kinematics Models
- Environment Models
- A.I. Models (Path Planning, Agents, ...)
• Simulation:
- Validate models
- Test robustness
- Improve design
Control:
Computational
Architectures
17
CONTROL:
=> From where do I start? Modeling and Simulation
Scientific
American
January 2007
Control:
Computational
Architectures
17
2
CONTROL:
C2
C3
C4
C1
C5
C0
• Sensorial Models
• Kinematics Models
M1
C7
M2
C6
Sensorial Model:
• Sonar
Kinematics Model:
• Infrared
• Differential
• Radar, Compass, Odometer
• Aeckerman
φ
Y
Aeckerman
θ
X
θ = V / L * Sin (Φ)
X = V * Cos (Φ) * Cos (θ)
Y = V * Cos (Φ) * Cos (θ)
Control:
Computational
Architectures
18
CONTROL:
• Robotic Control:
* Reactive
* Deliberative
* Hierarchical
* Hybrid
• Environment Maps
* Building Maps
* Path Planning
* SMPA - Sense Model Plan Act
• Problems:
* Complex tasks
* Avoid Obstacles: Static / Mobile - Unexpected obstacles
* Robot actual position estimation - Where am I ?
Control:
Computational
Architectures
17
3
CONTROL:
Complexity...
P.Bessièrre
* Action Planning
* Ability to Perceive the Environment
* Ability to Decide
* Ability to Act
* High Level Tasks Planning
* Reaction: Sensorial-Motor
* Estimate Actual and Future States
* Adaptation and Learning
* Robustness
* Unexpected Situations
=> From where do I start ???
Control:
Computational
Architectures
20
CONTROL:
Complexity...
Control:
Computational
Architectures
21
4
CONTROL:
Complexity...
Control:
Computational
Architectures
Simplify! How?
22
CONTROL: REACTIVE Architecture
Complexity...
Simplify! How?
MIT - OCW
• Reactive: Sensorial-Motor Integration
• Able to Act
• Able to Perceive the Environment
• Able to React
Reactive
Control
Architecture
23
5
S2
IF S1 < Threshold and
S2 < Threshold and
S3 < Threshold and
S4 < Threshold
THEN Action (Go_Forward)
S3
S4
S1
S0
S5
M1
S7
Reactive Control
M2
IF S1 < Threshold and
S2 < Threshold and
S3 > Threshold and
S4 > Threshold
THEN Action (Turn_Left)
S6
IF S2 > Threshold and
S3 > Threshold and
S2 > S3 and
S1 > S4
THEN Action (Turn_Right)
Sensorial-Motor: Perceive => Act
Reactive
Control
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Reactive Control
Robotic Lawn Mowers
- Toro iMow
- Husqvarna Auto Mower
- Automower Electrolux
Sensorial-Motor: Avoid Obstacles, Wall Following, Wander
Reactive
Control
Electrolux Trilobite
Robotic Vacuum Cleaner ZA1
http://www.onrobo.com/reviews/At_Home/Vacuum_Cleaners
http://www.onrobo.com/reviews/At_Home/Vacuum_Cleaners//
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6
Reactive Control
Sensorial-Motor:
- Avoid Obstacles
- Wall Following
- Wander
Simple behaviors…
Robustness? Complex tasks?
Reactive
Control
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CONTROL:
CONTROL: DELIBERATIVE Architectures
• Deliberative: Planning + Action
Deliberative Control
SIMROB (2D)
- Map
- Configuration Space
- Visibility Graph
- Optimized Path
(Dijkstra)
Robotic Arm:
Pre-defined paths
Deliberative
Control
Architecture
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7
Deliberative Control
CONTROL:
CONTROL: DELIBERATIVE Architectures
• Deliberative: Planning + Action
Tarefas
Complexas...
Robustez?
Imprevistos?
Ambiente pouco
conhecido?
Geometric Map based Navigation:
Planning: Graph+Dijkstra, A*
Grid based Navigation:
Planning: A*
Deliberative
Control
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CONTROL:
CONTROL: HIERARCHICAL and
HYBRID Architectures
Hierarchical and
Hybrid Control
Combining: Deliberative + Reactive
Hierarchical Control:
- Control Layers
- Priorities
- Information Exchange
Figures From:
Brooks, R. A.
MIT A.I. Memo 864
Sept. 1985
Brooks - Subsumption Architecture
Hierarchical
and Hybrid
Control
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8
Hierarchical and
Hybrid Control
CONTROL:
CONTROL: HIERARCHICAL and
HYBRID Architectures
Building the Environment Map:
MODEL
SMPA - SENSE / MODEL / PLAN / ACT
PLAN:
AStar
Dijkstra
S
E
N
S
E
ACT !
Sebastian Thrun / CMU
Hierarchical
and Hybrid
Control
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Hybrid Control
CONTROL:
CONTROL: Simple HYBRID Architectures
Farlei Heinen
PLAN: Dijkstra
ACT & ReACT
Hybrid
Control
31
9
Hybrid Control
CONTROL:
Farlei Heinen
References:
SEVA2D / SEVA3D
Autonomous
Vehicle Parking
TASK PLANNING & CONTROL:
Finite State Automata (FSA)
Artificial Neural Net (ANN)
ACTION:
Sense, Act
React (change state)
SEVA-A (Automaton)
Farlei Heinen
SEVA-N (Neural)
Farlei Heinen
Fernando Osório
Luciane Fortes
Milton Heinen
Publications:
SBRN 2002
WCCI 2006
Hybrid
Control
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Hybrid Control
CONTROL:
SimRob3D
SEVA3D
Simulation
Kinematics:
Estimation of
Position and Orientation
3D
World
Robot
Model
Control
Motor
Actions
Perception:
Sensor
Simulation Sensorial
Information
Commands
Control:
SEVA3D-A (FSA)
SEVA3D-N (Neural)
Sensors
Visualization
Hybrid
Control
32
10
CONTROL:
• Robotic Control:
* Reactive
* Deliberative
* Hierarchical
* Hybrid
• Environment Maps
* Building Maps
* Path Planning
* SMPA - Sense Model Plan Act
• Problems:
* Complex tasks
* Avoid Obstacles: Static / Mobile - Unexpected obstacles
* Robot actual position estimation - Where am I ?
Robust Hybrid
Control
33
Control System
Task Execution
PROBLEMS:
* Avoid Obstacles
- Known Obstacles
- Unknown Obstacles (static / no movement)
- Unknown Obstacles (dynamic / moving objects)
* Positioning
- How to determine the exact actual position of the robot ?
- How to maintain the control of exact position after displacement ?
- Error and Imprecision: Move forward / Rotate
Robust Hybrid
Control
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11
Control System
Task Execution
* Avoid Obstacles
PROBLEMS:
- Known Obstacles
- Unknown Obstacles (static / no movement)
- Unknown Obstacles (dynamic / moving objects)
Robust Hybrid
Control
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PROBLEMS:
Control System
Task Execution
* Positioning
- How to determine the exact actual position of the robot ?
- How to maintain the control of exact position after displacement ?
- Error and Imprecision: Move forward / Rotate
Robust Hybrid
Control
36
12
Robust Hybrid Control – COHBRA / HyCAR [SimRob3D]
Robot
Sensors
Actuators
Control Layers
Behaviors
Positioning
Estimator
(Monte Carlo)
Environment
Representation Maps
Polygonal
Sequencer
Control Modules
Grid
Path Planning
Topological
and
Semantic
Shared
Memory
Robust Hybrid
Control
Intelligent
COHBRA –Autonomous
Controle HíbridoVehicles
de Robôs Autônomos
HyCAR - Hybrid Control for Autonomous Robots
37
Simulation using SimRob3D
Robust Hybrid
Control
38
13
Simulation using a static environment
Position estimation based on Monte Carlo Localization Method
Robust Hybrid
Control
39
Position estimation based on Monte Carlo Localization Method
Robust Hybrid
Control
40
14
Environment was changed related to the original map
Internal robot representation is different from actual world configuration
Robust Hybrid
Control
41
Simulation using a dynamic environment (mobile obstacles)
Robust Hybrid
Control
42
15
Position estimation based on Monte Carlo Method:
Robot was moved, starting in a new and unknown position
Robust Hybrid
Control
43
Virtual Environment: 3D Realistic Environment
SimRob3D
Simulation
Tool
Robust Hybrid
Control
44
16
Virtual Environment: 3D Realistic Environment
SimRob3D
Simulation
Tool
Robust Hybrid
Control
45
SEVA 3D
SimRob3D
Simulation
Tool
Robust Hybrid
Control
46
17
Intelligent Autonomous Robots and Vehicles
<< Intelligence >>
* Task and Actions Planning
* Ability to Decide
* Ability to Act
* Reaction: Sensorial-Motor Integration
* Estimate Actual and Future States:
Environment + Behavior = Interaction
* Robustness: Unexpected Situations
Next steps...
GPVA
47
<< Intelligence >>
DARPA Challenge - Desert (2004, 2005)
* Ability to Decide
* Ability to Act
DARPA Challenge - Urban (2007)
Next steps...
GPVA
47
18
<< Intelligence >>
Computational Vision
* Ability to Decide
* Ability to Act
Next steps...
Computational
Vision
48
<< Intelligence >>
• Path following:
- Follow Me, Lane Follow
• Avoid danger situations: going out of the track
- Lane Detection
• Obstacle detection: pedestrians, cars, etc
• Traffic signs detection and recognition
• Visual Navigation (Based on Images)
Computational
Vision
49
19
<< Intelligence >>
Lane Follow
Lane Departure Detection
Follow Me
Computational
Vision
50
Visual Navigation
Image Database:
Path defined by a sequence of image
Reference
ImagemAtual
Image
NCC
(match)
Next Image
PróximaImagem
Imagem
Robot capturada
acquired
image
pelorobô
Navigation based on Monochromatic Images [Jones et al. 1997]
Algorithm: NCC – Normalized CrossCross-Correlation
Visual
Navigation
51
20
Visual Navigation
[Matsumoto et al. 1996]
Matlab Code
IR:
Reference Image
ICR:
Image Captured
by the robot
[Righes 2004, 2005]
Visual
Navigation
52
Visual Navigation
[Righes 04]
Visual
Navigation
53
21
Visual Navigation
Mobile Robot Localization
and Mapping with Uncertainty
using Scale-Invariant
Visual Landmarks
Stephen Se,
David Lowe,
Jim Little
(UBC, CA)
Algorithm:
SIFT
Reference
Int. Journal of Robotics Research
Vol. 21, No. 8, August 2002,
pp. 735-758,
Visual
Navigation
54
Visual Navigation
Omnidirectional
Cameras
Visual
Navigation
55
22
Aerial Visual Navigation
Vision System for Unmanned Aerial Vehicles
Correlation:
Satellite image
and Helicopter
Results...
Not good at all!
Visual
Navigation
55
Aerial Visual Navigation
Vision System for Unmanned Aerial Vehicles
Referential
Correlation in the Crossing Point
Using helicopter only images
Very
Good
Match!
Visual
Navigation
23
Vehicle Visual System
Vision system used to identify traffic signs
[F.Alves 2003]
[H]SI
RGB Color
HSV Color
Color based sign segmentation
Artificial Neural Network Recognition
Visual
Systems
Multiple Vehicles: Fire fighting squad
Planning, Navigation, Control + Strategy, Cooperation
Visual
Systems
24
Google:
Veículos
Autônomos
25

Autonomous Vehicles:

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