cs 4630: intelligent robotics and perception case study: motor schema-based design chapter 5 tucker...
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CS 4630: Intelligent Robotics and Perception
Case Study: Motor Schema-based DesignChapter 5
Tucker Balch
AMiREOctober, 2001
Tucker BalchGeorgia Institute of Technology
What We’ve Covered
• History of Intelligent Robotics (Chapter 1)
• Hierarchical paradigm (Chapter 2)• Biological basis for behavior-based
control (Chapter 3)• Overview of behavior based control
(Chapter 4)• Subsumption architecture (Chapter 4)• Motor schema-based control (Chapter
4)
AMiREOctober, 2001
Tucker BalchGeorgia Institute of Technology
Upcoming
• Today: case study of behavior-based control for multirobot team.
• Friday: TeamBots tutorial, new project assignment
• Monday: Midterm Exam• Weds: Begin Chapter 5 (Sensing)• Friday: Guest Lecture (Koenig)
AMiREOctober, 2001
Tucker BalchGeorgia Institute of Technology
Social Potentials
Balch & Arkin, IEEE Transactions on Robotics and Automation, 2000
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The Multi-Foraging Task
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Foraging Robots (1997)
• Balch, AI Magazine, 1997.• Balch, Autonomous Robots, 2000.
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Tucker BalchGeorgia Institute of Technology
Foraging Robots (1997)
• Balch, AI Magazine, 1997.• Balch, Autonomous Robots, 2000.
AMiREOctober, 2001
Tucker BalchGeorgia Institute of Technology
Foraging Robots (1997)
• Balch, AI Magazine, 1997.• Balch, Autonomous Robots, 2000.
AMiREOctober, 2001
Tucker BalchGeorgia Institute of Technology
Foraging Robots (1997)
• Balch, AI Magazine, 1997.• Balch, Autonomous Robots, 2000.
AMiREOctober, 2001
Tucker BalchGeorgia Institute of Technology
Behavioral Sequencing
Search
Deliver Redhave red
Aquire Red
see red
~see red~have red
at red bin
Acquire Blue Deliver Bluehave bluesee blue
at blue bin
~see blue
~have blue
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Performance as Team Size Increases
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Problem: Inter-Robot Interference
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Heterogeneous Strategy 1: Specialization
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Heterogeneous Strategy 2: Territorial
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Performance Comparison
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Are Diversity and Performance Correlated?
• Need a measure of robot team diversity
• Approach: information theory
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Diversity and Performance Negatively Correlated in Foraging
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Diversity and Performance Positively Correlated in Soccer
Homogeneous Team Heterogeneous Team
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AMiREOctober, 2001
Tucker BalchGeorgia Institute of Technology
Where We are
Real-time Video Processing
Behavioral Sequence Representation
Learning Algorithms
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Tucker BalchGeorgia Institute of Technology
Observing and Modeling Live Multi-Agent Systems
• Motivation– Our agents should act
intelligently in the presence of other agents: humans, external agents, adversaries
• Social insects: – Rich, multiagent interactions– Adversarial/territorial behaviors– Real biology in collaboration
with entomologists
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Research Goal: Develop Algorithms That Enable
• Simultaneous tracking of all the individuals in a colony
• Recognition of individual and colony behaviors
• Learning of new single and multi-agent behavior models
• Application of the models to multi-agent software and robotic systems
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The complexity of ant society
Holldobler & Wilson, 1990Gordon, 1999
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Video of ant behaviors
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Tucker BalchGeorgia Institute of Technology
Finding Ants In Images (1)
• CMVision: Color-based tracking– Initially developed for tracking soccer
robots– Classifies and segments regions
according to color– 100s of regions, 32 colors, 30Hz, low
cost
Bruce, Balch & Veloso, IROS-2000
AMiREOctober, 2001
Tucker BalchGeorgia Institute of Technology
Finding ants in images (2)
• Approach: background subtraction
• Enables classification by color
and motion
Bij = (1 - )Bij + Iij
Background Image Current Image Movement
AMiREOctober, 2001
Tucker BalchGeorgia Institute of Technology
Associating observations with individuals• The association problem
– Best optimal algorithm O(n3)– Greedy approach O(n2)
• Noisy data presents additional challenges– Splitting, merging, drop-outs, pop-ups
• Current approach– “Greedy agents” leverage domain knowledge
• Future– Parallel implementations, Bayesian techniques
(e.g. Xiang & Lesser), radar tracking techniques
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Tucker BalchGeorgia Institute of Technology
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Analyzing the Spatial Behavior of a Colony
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No Food Available
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Food Available
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Vector Representation
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Recognition Task: Right Turn, Left Turn, Straight?
• Approach: Average turning angles over a window
• Classify turns according to average:– if A < -, right turn– if A > , left turn– otherwise, straight
1 2 3 4 5 6 7 8 9 10
in
A1
n is the window size
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Example
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Recognizing Behavior from Movement Traces• Hypothesis:
– Observed movement features considered over time can be used to classify the behavior of a physical agent
• Previous success in observation of soccer agents– Hidden Markov Models (Han & Veloso, 1999)
• Example features– binary: towards-food, at-food, towards-home, at-
home– continuous: velocity, turn-rate, path randomness
• Example behaviors– foraging, patrolling, carrying, recruiting
AMiREOctober, 2001
Tucker BalchGeorgia Institute of Technology
Hidden Markov Model Representation
S1 S3S20.9 0.1
0.90.1 0.1
0.9
A B C
AAABBBBBBBBBBBBCCABBBBBBBBBCCA
AMiREOctober, 2001
Tucker BalchGeorgia Institute of Technology
Hidden Markov Model Representation
S1 S3S20.9 0.1
0.90.1 0.1
0.9
A 0.8B 0.1C 0.1
A 0.1B 0.8C 0.1
A 0.1B 0.1C 0.8
ACABBBABBBCBBBBCAABBBBABBBBCCA
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Tucker BalchGeorgia Institute of Technology
Using HMMs for Recognition With the Viterbi Algorithm
AAABBBBBBBBBBBBCCABBBBBBBBBCCA
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inspired by Han & Veloso, 1999
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Real-time Video Processing
Behavioral Sequence Representation
Recognition AlgorithmsLearning Algorithms
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Tucker BalchGeorgia Institute of Technology
Thanks to• Zia Khan• Manuela Veloso• James Bruce• Gak Kaminka• Pat Riley• Rande Shern• Ashley Stroupe
• DARPA Control of Agent Based Systems (CoABS)
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http://www.cc.gatech.edu/~tucker
AMiREOctober, 2001
Tucker BalchGeorgia Institute of Technology
• www.cc.gatech.edu/~tucker• www.cc.gatech.edu/~cprl
Observing Ants: Tracking and Analyzing the Behavior of Live Insects
Tucker BalchCollaborative Perception and Robotics Lab
AMiREOctober, 2001
Tucker BalchGeorgia Institute of Technology