Pursuit Evasion Games PEGs Using a Sensor Network

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Pursuit Evasion Games (PEGs) Using a Sensor
Network

• Luca Schenato, Bruno Sinopoli
• Robotics and Intelligent Machines Laboratory
• UC Berkeley
• sinopoli,lusche_at_eecs.berkeley.edu

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Outline

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

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Current Experimental Setup for PEG

• 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
Aerial Pursuer
Vehicle Position Vision Sensor
Waypt Request
Ground Pursuer
3x3m Camera View
GroundEvader
Vehicle Position Vision Sensor
Centralized Ground Station
Courtesy of Jin Kim
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Current PEG Implementation
UAVs

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

Lucent Orinoco (WaveLAN) (Ad Hoc Mode)
Ground Monitoring System
Ground Mobile Robots
Gateways
Sensor Webs
Courtesy of Jin Kim
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Distributed Pursuit Evasion Games (DPEG)
Robot pictures from ActivMedia website
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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

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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

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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
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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

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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 (lt100 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.

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What we need to do in the short term

• Lets do the real thing!!!
• Select a big enough space (RFS)
• Deploy, test and debug a network of sensors
  (gt400)
• Start with centralized algorithm
• Use the test-bed to evaluate algorithms and
  bootstrap any interesting research projects
• Continue with decentralized algorithms

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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

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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)

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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