Networks of Autonomous Unmanned Vehicles

1. Networks of Autonomous Unmanned Vehicles Prof. Schwartz Prof. Esfandiari Prof. P. Liu Prof. P. Staznicky

2. Research and Development Areas • Autonomous Robot Construction. • Cooperating Mobile Autonomous Robots. • Vision Systems. • Robot Flocking and Swarming • Robot swarms that adapt and learn (game theory and evolution). • Robot teams and learning.

3. Autonomous Vehicles Built from low cost robot kit. HandyBoard HC11 controller Bluetooth communication channel. Sonar sensor. Able to control over internet. On board navigation control.

4. Robotic Tracking • Activmedia PeopleBot Robot • 2 DOF camera • Optical flow-based target detection and verification • Target’s motion is estimated using a particle filter • Laser rangefinder • It is used to determine distance between robot and target

5. Robotic Boat • developed by 4th-year students • The boat can be controlled over a wireless network • User with a PC and a web browser can control the boat from anywhere • The web server is placed on the on-board microcontroller, which has not be done before by others

6. Unmanned Aircraft System (UAS) Development • UAS for geophysical surveys is being developed in the Mechanical and aerospace Engineering department (M&AE), with industry partner, an Ottawa company and with support from Systems and Computer Engineering (SCE) • UAS has a demanding mission • 8-hours endurance • Airspeed between 60 and 100 kts • Low altitude down to 30 ft above terrain; terrain following is required • Sensitive magnetometers are mounted on the wingtips • Magnetic signature of the air vehicle must be minimized

7. UAS Development: Status • Air Vehicle prototype is being built • Size: Wing span 16 ft, weight 200 lb, engine power 30 hp • Start of flight testing: spring of 2009 • Four research projects are underway, collaboration between M&AE and SCE: • Autonomous operation • Obstacle detection and avoidance • Magnetic signature control • Low-cost non-magnetic airframe

8. UAS Development: Experimentation • Two small aircraft, avionics test beds, are being used for testing • Autopilot system • Telemetry system • Communication system • Iridium satellite system selected and being tested • Altimeter system • A laser altimeter has been purchased for testing

9. Robots leaving a room using game theory

10. Modelling

11. Cooperative robots and intelligence • Robots have own control and navigation algorithms • Robots only know their position and others

12. Video Processing and Understanding Tracking of video objects

13. Intelligent Video Object Tracking Tracking, counting and timing of video objects.

14. Networks of Robots and Sensor Swarms

15. Vehicles Playing the Evader – Pursuit Game • Research Topics • Vehicles Learn Each Others Dynamics. • Vehicles Adapt Behaviour. • Coalition and Team Formation

16. Soccer Playing Robots • We are interested in imitating agent behavior that is space and time dependent • RoboCup is a good environment for such exploration Our methodology: • Perform data capture from logs generated by existing RoboCup clients • Transform the captured data into a spatial knowledge representation format (a scene) • Game-time: pick closest (or one of k-closest) captured scene to current one and perform corresponding action

17. Robots Learning How to Play Soccer

18. Scene Recognition “What should I do in this situation?” “What did the observed agent do when faced with a situation like this?” • Find best match(es) between current situation and stored scenes (k-nearest-neighbor search) • Perform associated action -> accuracy of the distance calculation function between two scenes is crucial

19. The Robots Have Learned the Game

20. Limitations and Future Work • Short term • consider object velocity; • weigh the importance of an object based on its proximity to the player; • scene prototyping to reduce duplication and introduce more scene variation; (done!) • CBR-style adaptation of the action; • automatic weight determination is very time consuming: more tests required here. (done!) • Long term • Need to take into account state and context-based behavior: • non-visual info: body state, game state... • actions as part of a plan or succession of scenes • a clue: two similar scenes leading to different actions • might need to remember and backtrack to previous scene(s) • Higher-level representation for scenes • conversion to spatial and/or temporal logic?

21. Multiple Robots

22. Actions • Follow up-hill gradient • Or follow the down-hill gradient

23. Personality rewards • Courage: • Fear • Cooperation

24. Algorithm • Calculate up-hill and down-hill gradients • Calculate personality rewards • Calculate if robot has been shot. If so, go back to base. • Update personalities: • Where η is the learning rate and the step size is:

25. Personalities dynamics

26. Swarm Intelligence and Personality Evolution • Game Theory, Coalition formation. • Evolutionary Game Theory. • Learning (fuzzy, adaptive, genetic). • Personality Traits.

27. This is a smart robot

28. Conclusion • Capability in Building Autonomous Vehicles • Autonomous Vehicle Control • Swarming • Evader/Pursuer • Learning and Adapting Networks • Robots leaving a room • Learning to play soccer • adapting personalities