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
• Targets 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 (MAE), 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 MAE 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
Robots leaving a room Robots leaving a room Player B Player B
Robots leaving a room Robots leaving a room Walk Wait
Player A Walk -1 X
Player A Wait -1 0
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
• -gt 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 ? 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