SLIDE 1
Cooperative control of multi-vehicle systems
Sonia Mart´ ınez
Mechanical and Aerospace Engineering Jacobs School of Engineering University of California at San Diego http://faemino.ucsd.edu/soniamartinez/index.html
,
Cooperative control of multi-vehicle systems Sonia Mart nez - - PowerPoint PPT Presentation
Cooperative control of multi-vehicle systems Sonia Mart nez - - PowerPoint PPT Presentation
Cooperative control of multi-vehicle systems Sonia Mart nez Mechanical and Aerospace Engineering Jacobs School of Engineering University of California at San Diego http://faemino.ucsd.edu/soniamartinez/index.html B-DRASS seminar, The
SLIDE 2
SLIDE 3
,
Self-organized behaviors in biological groups
Natural multi-agent systems:
SLIDE 4
,
Decision making in animals
Able to deploy over a given region assume specified pattern rendezvous at a common point jointly initiate motion/change direction in a synchronized way Species achieve synchronized behavior with limited sensing/communication between individuals without apparently following group leader
(Couzin et al, Nature 05; Conradt et al, Nature 03)
SLIDE 5
,
Emerging technologies and multi-agent systems
Distributed habitat/environment monitoring systems
Great Duck Island
-
-
- Scripps multi-UAV project, UCSD
ASAP project illustration
Disaster relief operations, rapid deployment systems, surveillance Health monitoring in smart bridges, buildings, roads
Soil monitoring
25 Motes on Damaged sidewall Wind Response Of Golden Gate Bridge surveillance Berkeley motes Sandia National Labs Berkeley
SLIDE 6
,
Research challenges
What useful engineering tasks can be performed with limited-sensing/communication agents? Dynamics simple interactions give rise to rich emerging behavior Feedback rather than open-loop computation for known/static setup Information flow who knows what, when, why, how, dynamically changing Reliability/performance robust, efficient, predictable behavior How to coordinate individual agents into coherent whole? Objective: systematic methodologies to design and analyze cooperative strategies to control multi-agent systems
SLIDE 7
,
Research program: What are we after?
Development of system-theoretic tools to analyze general coordination algorithms under disturbances Synthesis of distributed motion coordination algorithms that are flexible, reconfigurable, scalable and verifiably correct
-
-
- Synthesis of distributed filters and learning algorithms
to allow for distributed and adaptive decision making Testbed development for technology transition
SLIDE 8
,
Text: Distributed Control of Robotic Networks
1 intro to distributed algorithms
(graph theory, synchronous networks, and averaging algos)
2 geometric models and geometric
- ptimization problems
3 model for robotic, relative sensing
networks, and complexity
4 algorithms for rendezvous,
deployment, boundary estimation Status: Freely downloadable at http://coordinationbook.info with tutorial slides & software libraries. Shortly on sale by Princeton Univ Press
SLIDE 9
,
Outline
1 Motivation 2 Sample motion coordination algorithms and results
Multi-vehicle Deployment & Task Assignment Multi-vehicle Rendezvous Motion coordination for optimal estimation
3 Conclusions and Future Work
SLIDE 10
,
Deployment algorithms
Goal: Given n agents in an environment Q, achieve optimal coverage by designing ˙ pi = ui, i ∈ {1, . . . , n} Metrics capturing “good deployment” Area jointly covered — limited sensing ranges Expected network cost to visit locations in Q: constrained dynamics (motion constraints, environmental flows) limited energy budget for each agent to move Local Rules can be synthesized that are:
1 locally efficient, distributed, lead to stable emerging behaviors 2 robust to failures and asynchronous computations
SLIDE 11
,
Rendezvous algorithms
Goal: Given nodes (p1, . . . , pn) moving in environment Q, gather at a given location of Q Metrics capturing “rendezvous” objective Disagreement in position Diameter of network Local Rules can be synthesized that:
1 Achieve task under switching connectivity conditions 2 Totally distributed 3 Work under rather persistent communication/sensing failures 4 Are robust to measurement noise and asynchronous behavior
SLIDE 12
,
Motion coordination for optimal estimation
Goal 1: Track a moving target q0 Goal 2: Track an evolving boundary Information metrics Fisher Information metrics Goodness-of-interpolation metrics Local Rules can be synthesized that are:
1 Distributed, able to track a global/local maxima (bounded error
depending on the velocity of change)
2 Robust to communication/sensing failures 3 Robust to asynchronous behaviors
SLIDE 13
,
Sample simulations
SLIDE 14
,
Summary
Basic motion coordination algorithms for multiagent systems sample primitives: deployment, rendezvous, optimal estimation System-theoretic tools for design: proximity graphs, graph theory distributed algorithms set-valued maps, invariance principles . . . Features of algorithms: scalable, adaptive, verifiably correct, robust, distributed
SLIDE 15
,
Current and Future work
Energy-efficient coordination algorithms Motion coordination partially known, time-varying environments Distributed optimization and learning for multi-agent systems
Selfish and cooperative multi-agent behaviors Distributed receding-horizon control policies
Coordination of multiple vehicles under communication limitations Adversarial multi-robot teams
SLIDE 16
,