Pursuit Evasion Games (PEGs) Using a Sensor Network Luca Schenato, Bruno Sinopoli Robotics and Intelligent Machines Laboratory UC Berkeley

Outline Description of the application The role of a sensor network Implementation issues Open problems Tentative roadmap

Aerial Pursuer Current Experimental Setup for PEG Centralized Ground Station Experiment Setup -Cooperation of -One Aerial Pursuer (Ursa Magna 2) -Three Ground Pursuer (Pioneer UGV) -Against One Ground Evader (Pioneer UGV) (Random or Counter-intelligent Motion) -Wireless Peer-to-Peer Network Arena: Cell: 1m x 1m Detection: Vision-based or simulated Ground Evader Ground Pursuer 3x3m Camera View Waypt Request Vehicle Position Vision Sensor Vehicle Position Vision Sensor Courtesy of Jin Kim

Current PEG Implementation Ground Monitoring System Ground Mobile Robots UAVs Courtesy of Jin Kim Lucent Orinoco (WaveLAN) (Ad Hoc Mode)

Where does the Sensor Network fit in? Ground Monitoring System Ground Mobile Robots UAVs Sensor Webs Gateways Courtesy of Jin Kim Lucent Orinoco (WaveLAN) (Ad Hoc Mode)

Distributed Pursuit Evasion Games (DPEG) * Robot pictures from ActivMedia website

The role of a sensor network Provide additional information about evaders’ motion Relay such information to the pursuers to design and implement an optimal pursue strategy Possibly provide guidance to pursuers

The general picture Sensors: –randomly distributed –partial location information –limited communication range and bandwidth, which depends heavily on the topology of the environment –limited computation power Network: –Ad hoc –Dynamic network topology –Multi hop communication

Implementation Issues The complexity of the problem suggests an incremental approach to implementation: –Debugging is problematic and costly –Too many things can go wrong at the same time –Extremely difficult to analyze algorithms for the general framework. Divide & Conquer

Implementation Strategy Implement & test algorithms within TOSSIM Interface between TOSSIM and Matlab for visualization purposes Evaluate performances with respect to key objectives: –Accuracy –Power usage –Security –Robustness –Bandwidth efficiency

Approach to experiment Start with simplified version of the full scale application, i.e. : –Assume motes know their position –Assume robots know their position and move on straight lines at a constant velocity Debug algorithms on a subnet (<100 nodes) Add new algorithms as they become available Develop a monitoring system to track the state of the network –Routing tables, connectivity, data passing etc.

What we need to do in the short term Let’s do the real thing!!! –Select a big enough space (RFS) –Deploy, test and debug a network of sensors (>=400) –Start with centralized algorithm –Use the test-bed to evaluate algorithms and bootstrap any interesting research projects –Continue with decentralized algorithms

Why starting with centralized approach ? Algorithms are ready It will show if, when and why centralized algorithms fail It will inspire decentralized algorithms Feasible by January

In the long term: Interface TOSSIM with a visualization tool and test decentralized algorithms Implement the most promising on the test-bed (ideally by January)

Would be nice if: Motes were self programming There was monitoring system There was a GUI There was a smart powering system There was a “loose” synchronization scheme to avoid clock drift