Networks of Autonomous Unmanned Vehicles

Prof. SchwartzProf. Esfandiari

Prof. P. LiuProf. P. Staznicky

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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.

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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.

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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

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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

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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

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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

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Cooperative robots and intelligence• Robots have own control and navigation algorithms

• Robots only know their position and others

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Video Processing and Understanding

Tracking of video objects

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Networks of Robots and Sensor Swarms

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Robots that follow each other and avoid obstacles

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Vehicles Playing the Evader – Pursuit Game

Research Topics

• Vehicles Learn Each Others Dynamics.

• Vehicles Adapt Behaviour.

• Coalition and Team Formation

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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:

1. Perform data capture from logs generated by existing RoboCup clients

2. Transform the captured data into a spatial knowledge representation format (a scene)

3. Game-time: pick closest (or one of k-closest) captured scene to current one and perform corresponding action

We are interested in imitating agent behavior that is space and time dependent RoboCup is a good environment for such exploration

Our methodology:

1. Perform data capture from logs generated by existing RoboCup clients

2. Transform the captured data into a spatial knowledge representation format (a scene)

3. Game-time: pick closest (or one of k-closest) captured scene to current one and perform corresponding action

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Robots Learning How to Play Soccer

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Scene RecognitionScene Recognition

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

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

“What should I do in this situation?”

“What did the observed agent do when faced with a situation like this?”

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The Robots Have Learned the Game

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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?

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Multiple Robots

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

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This is a smart robot

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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