Cooperative Control of Distributed Autonomous Vehicles in Adversarial Environments

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Cooperative Control of Distributed Autonomous
Vehicles in Adversarial Environments

• AFOSR 2002
• MURI Annual Review
• Caltech/Cornell/MIT/UCLA
• June 4, 2002

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Mission Networks of (semi) Autonomous Vehicles
Challenges Local information/decision making Constrained communications Large scale of operations Uncertain dynamic environment Hostile adversarial presence
Approach Multidisciplinary Research Multiscale Modeling Hierarchical Planning Logical Programming Environments Complexity Management Distributed Protocols Language Adaptation Biological Modeling Experimentation Case Study Simulations Hybrid Hardware Realization
Goal Deployment of Large Scale Networks of (semi) Autonomous Vehicles
Complex Collective Behavior from Simple Individual Behavior
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Research Focus

• Scalability, modeling reduction
• Representation of distributed low level
  components in a manner amenable to high level
  planning with reduced complexity.
• High level planning
• Development of analytical methods and
  computational algorithms for coordinated team
  strategies.
• Low level execution
• Realization of team strategies through low level
  strategies and optimization.
• Communications
• Investigation of communications issues within
  and among levels.

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Expected Outcomes

• Theory Analytical understanding of achievable
  performance of distributed cooperative control
  systems.
• Computation Algorithms software tools for
  control design, testing, evaluation, and rapid
  prototyping.
• Experimentation Application to simulated and
  hardware testbeds.
• Education Multidisciplinary program with
  increased DoD visibility.

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Expected Insights

• How to address scalability through modeling
  decomposition.
• How to address computational complexity in
  hierarchical designs.
• How to develop reliable multi-layered cooperative
  strategies.
• How to counter adversarial actions with
  constrained communications.
• How to integrate local optimizations for
  collective performance.
• How to synchronize cooperating elements through
  modeling and ID.
• How to exploit neurological models to design
  cooperating elements.
• How to achieve reliable communications in
  hierarchical structures.
• How to derive adaptive languages for autonomous
  operations.

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Scalability, Modeling Reduction

• Klavins, Caltech
• Complexity burden of coordination on
  communications
• Gomes, Cornell
• Strategies to scale solutions of combinatorial
  problems arising in cooperative control

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High Level Planning

• Speyer, UCLA
• Implications of partial unshared information in
  cooperative and noncooperative control
• Hickey, Caltech
• Robust programming languages for implementing
  embedded control software
• DAndrea, Cornell
• Probability map building for multi-vehicle path
  planning

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Low Level Execution

• Murray, Caltech
• Potential functions to provide virtual shaping
  of vehicle formations
• Massaquoi, MIT
• Basal ganglia based modeling of upper lower
  loop motion control

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Communications

• Pottie, UCLA
• Channel capacity of networks consisting of
  one-hop clusters and mobile multi-hop backbone
• Taylor, UCLA
• Adaptive languages for UAVs to communicate among
  themselves and other autonomous systems

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Team Profile
Caltech 2 co-PIs (CDS, CS) 2 postdocs 2 graduate students Cornell 3 co-PIs (MAE, CS) 1 postdoc 3 graduatestudents
MIT 4 co-PIs (EECS, AA, Neuro) 1 postdoc 4 students UCLA 5 co-PIs (AE, EE, MAE, Bio) 5 students
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Collaborations Interactions

• MURI Minisymposium February 2002
• DARPA/MICA Program Transition Motivation
• Caltech/Cornell/MIT Reading Group
• Caltech/Cornell SURF Project (MICA)

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Case Studies

• Multi-vehicle tasking with obstacle and mutual
  avoidance (one-sided)
• Roboflag (two-sided/vehicle)
• Autonomous suppression of enemy defenses (MICA
  motivated)

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Experimental Testbeds

• Cornell Roboflag

• Caltech MVWT

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Agenda
800-830 Continental Breakfast Registration
830-845 Opening Remarks King (AFOSR)
845-915 Overview Shamma (UCLA)
915-1015 Coordinated Multi-vehicle Operations Dahleh/Kulkarni (MIT)
Dynamic Adversarial Conflict with Restricted Information Speyer (UCLA)
1015-1030 Break
1030-1200 Communication Complexity of Multi-vehicle Systems Klavins (Caltech)
Channel Capacity Issues for Mobile Teams Pottie (UCLA)
Language Acquisition by Distributed Agents Taylor (UCLA)
1200-130 Lunch
130-300 Distributed Control of Multi-Vehicle Systems Murray/Hickey (Caltech)
Cooperative Vehicle Control DAndrea (Cornell)
300-315 Break
315-415 Combinatorial Problems in Cooperative Control Gomes (Cornell)
The Role of the Basal Ganglia in Motor Control Massaquoi/Mao (MIT)
415-500 Open Discussion