1. Cooperative Control of Distributed Autonomous Vehicles in Adversarial Environments AFOSR 2002 MURI Annual Review Caltech/Cornell/MIT/UCLA June 4, 2002

2. 2 Mission: Networks of (semi) Autonomous Vehicles Goal Deployment of Large Scale Networks of (semi) Autonomous Vehicles Complex Collective Behavior from Simple Individual Behavior Challenges Local information/decision making Constrained communications Large scale of operations Uncertain dynamic environment Hostile adversarial presenceApproach Multidisciplinary Research: Multidisciplinary Research: Multiscale Modeling + Hierarchical Planning + Logical Programming Environments + Complexity Management + Distributed Protocols + Language Adaptation + Biological Modeling Experimentation: Experimentation: Case Study Simulations + Hybrid Hardware Realization

3. 3 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.

4. 4 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.

5. 5 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.

6. 6 Scalability, Modeling & Reduction Klavins, Caltech: Complexity burden of coordination on communications Gomes, Cornell: Strategies to scale solutions of combinatorial problems arising in cooperative control

7. 7 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 D’Andrea, Cornell: Probability map building for multi-vehicle path planning

8. 8 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

9. 9 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

10. 10 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

11. 11 Collaborations & Interactions MURI Minisymposium: February 2002 DARPA/MICA Program: Transition & Motivation Caltech/Cornell/MIT Reading Group Caltech/Cornell SURF Project (MICA)

12. 12 Case Studies Multi-vehicle tasking with obstacle and mutual avoidance (one-sided) Roboflag (two-sided/vehicle) Autonomous suppression of enemy defenses (MICA motivated)

13. 13 Experimental Testbeds Cornell Roboflag: Caltech MVWT:

14. 14 Agenda 8:00-8:30Continental Breakfast & Registration 8:30-8:45“Opening Remarks”King (AFOSR) 8:45-9:15“Overview”Shamma (UCLA) 9:15-10:15“Coordinated Multi-vehicle Operations”Dahleh/Kulkarni (MIT) “Dynamic Adversarial Conflict with Restricted Information” Speyer (UCLA) 10:15-10:30Break 10:30-12:00“Communication Complexity of Multi-vehicle Systems”Klavins (Caltech) “Channel Capacity Issues for Mobile Teams”Pottie (UCLA) “Language Acquisition by Distributed Agents”Taylor (UCLA) 12:00-1:30Lunch 1:30-3:00“Distributed Control of Multi-Vehicle Systems”Murray/Hickey (Caltech) “Cooperative Vehicle Control”D’Andrea (Cornell) 3:00-3:15Break 3:15-4:15“Combinatorial Problems in Cooperative Control”Gomes (Cornell) “The Role of the Basal Ganglia in Motor Control”Massaquoi/Mao (MIT) 4:15-5:00Open Discussion