Dr. Shankar Sastry, Chair Electrical Engineering & Computer Sciences University of California, Berkeley

Sub-problems for PEG  Sensing – Navigation sensors -> Self-localization – Detection of objects of interest  Framework for communication and data flow  Map building of environments and evaders – How to incorporate sensed data into agents’ belief states  probability distribution over the state space of the world (I.e. possible configuration of locations of agents and obstacles) – How to update belief states  Strategy planning – Computation of pursuit policy  mapping from the belief state to the action space  Control / Action SENSOR NETWORKS

Localization & Map building  Localization : updating agent’s position relative to the environment  Map building: updating object locations relative to the agent’s position or to the environment  They can be benefited from different techniques, e.g., Occupancy-based : well-suited to path planning, navigation, and obstacle avoidance, expensive algorithms(e.g. pattern matching) required for localization Beacon-based : successful to localization Fail in cluttered environment, unknown types of objects

How Sensor Web can help?  Current BEAR Framework for PEG – Navigation sensors(INS, GPS, ultrasonic sensor…) for localization – Ultrasonic sensor for obstacle avoidance – Vision-based detection for moving targets (enemy) – Occupancy-based map building for planning  Potential Issues for real-world PEG – GPS jamming, unbounded error of INS, noisy ultrasonic sensors – Computer vision algorithms are expensive – Unmanned vehicles are expensive  It is unrealistic to employ many number of unmanned vehicles to cover a large region to be monitored.  Static optimal placement of unmanned vehicles for cooperative observations are already difficult (e.g. art-gallery or vertex-cover problems).

actuator positions inertial positions height over terrain obstacles detected targets detected control signals INSGPS ultrasonic altimeter vision state of agents obstacles detected targets detected obstacles detected agents positions desired agents actions Tactical Planner & Regulation Vehicle-level sensor fusion Strategy PlannerMap Builder position of targets position of obstacles positions of agents Communications Network tactical planner trajectory planner regulation lin. accel. ang. vel. Targets Exogenous disturbance UAV dynamics Terrain actuator encoder s UGV dynamics NEST SENSORS objects detected

Pursuit-Evasion Game Experiment Setup Ground Command Post Waypoint Command Current Position, Vehicle Stats Pursuer: UAV Evader: UGV Evader location detected by Vision system

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

Experimental Results: Pursuit-Evasion Games with 4UGVs and 1 UAV (Spring’ 01)

Sensor Webs in BEAR Network Ground Monitoring System Landing Decks Ground Mobile Robots UAVs Lucent Orinoco (WaveLAN) (Ad Hoc Mode) Sensor Webs Gateways

Sensor Nets for Map Building & PEG Necessary information for map building and PEG Binary detection + time stamp + ID(or position) of the node Sensing Platform Time- synchronization Self- localization

Abstraction of Sensor Nets  Properties of general sensor nodes are described by – sensing range, confidence on the sensed data – memory, computation capability – Clock skew – Communication range, bandwidth, time delay, transmission loss – broadcasting methods (periodic or event-based) – And more…  To apply sensor nodes for the experiments with BEAR platform, introduce super-nodes ( or gateways ), which can – gather information from sub-nodes ( filtering or fusion of the data from sub-nodes for partial map building) – communicate with UAV/UGVs

Roadmap towards complete PEG Experiments I. N nodes uniformly distributed in each cell in an N-grid environment, e.g, 400 nodes placed in each 1-by-1 m cell for 20x20 meter flat surface at RFS. ( test self-localization and detection, and integrate with BEAR platform ) II. N n nodes randomly placed, with known positions (capture time vs.N n ) III. N n nodes randomly placed, with unknown positions