August 31, Reactive Algorithms I

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August 31, Reactive Algorithms I

  1. 1. Multi-Robot Systems<br />CSCI 7000-006<br />Monday, August 31, 2009<br />NikolausCorrell<br />
  2. 2. So far<br />Introduction to robotics and multi-robot systems<br />Similar algorithms and properties for robot teams, robot swarms and smart materials<br />
  3. 3. Today<br />Reactive algorithms<br />Environmental templates<br />Collaboration in reactive swarms<br />
  4. 4. Reactive Algorithms<br />Directly couple perception to action<br />Extremely simple hardware (analog electronics will do)<br />Robustness out of simplicity<br />Potential for miniaturization<br />First instance: Grey Walter’s tortoises<br />© The i-Swarm project<br />
  5. 5. Concept: Braitenberg Vehicles<br />Couple perception to action<br />Sensor input coupled to actuator output<br />Inspired by brain architecture <br />left/right hemisphere<br />Neural network<br />Course question: how do the vehicles behave with respect to a light source?<br />Light Sensor<br />Motors<br />
  6. 6. More complex behaviors<br />Braitenberg<br />More sensors (e.g. camera)<br />More connections (e.g. brain)<br />Synthesis by genetic algorithms<br />Modify random connections<br />Unfit individuals fall of the table<br />Hierarchical Decompositon<br />
  7. 7. Subsumption Architecture (Brooks)<br />Decompose behavior into modules<br />Collision avoidance, light following, etc.<br />Arrange modules in layers representing goals<br />Upper layers subsume lower layers<br />Difficult to design with increasing complexity<br />Explore world<br />Wander around<br />Avoid Obstacles<br />Brooks, R. (1986). &quot;A robust layered control system for a mobile robot&quot;. Robotics and Automation, IEEE Journal of 2 (1): 14–23.<br />
  8. 8. Alternative view: Artificial Potential Fields<br />Aka virtual physics, motor schemes<br />Goals are represented by virtual forces (attraction/repulsion)<br /> Forces are calculated from sensor input<br />Addition yields vector field that the robots follow<br />Obvious problem: local minima and cycles<br />© Craig Reynolds<br />
  9. 9. Further Reading<br />ValentionBraitenberg“Experiments in synthetic psychology”, 1986<br />Rodney Brooks“Elephants don’t play chess”, 1990<br />Ronald Arkin“Behavior-based Robotics”, 1998<br />
  10. 10. Example: Jet Turbine Inspection<br />Goal: surround every blade in a turbine with a robotic sensor<br />Robots need to be small, only local communication<br />Alice(ASL, EPFL), sugar cube, 368bytes of RAM<br />
  11. 11. Robotic Platform<br />Alice miniature robot [Caprari2005]<br />PIC microcontroller (368 bytes RAM, 8Kb FLASH)<br />Length of 22mm<br />Maximal speed of 4cm/s, stepper motors<br />4 IR modules serve as very crude proximity sensors (3cm) and local communication devices <br />Energetic autonomy 5h-10h<br />
  12. 12. Baseline: Randomized Coverage without Localization<br />Search<br />Inspect<br />Translate<br />Avoid Obstacle<br />Wall | Robot<br />Obstacle clear<br />Search<br />Inspect<br />Translate<br />along blade<br />pt<br />Blade<br />1-pt<br />Tt expired<br />
  13. 13. Robot Capabilities<br />Sensing: infrared distance sensors<br />Computation: FSM, wall following<br />Actuation: differential wheels<br />Communication: none<br />
  14. 14. Analysis (Intuition)<br />Collaboration: implicit<br />Completeness: probabilistic, asymptotic<br />Probability to leave blade at round or sharp tip affects robot distribution<br />
  15. 15. Experimental Results<br />20, 25, 30 robots<br />
  16. 16. Spatial distribution for pt=0<br />Leaving the blades at a tip generates drift in the environment<br />“Enviromental Template”<br />Probability to inspect some of the blades higher<br />
  17. 17. Exploiting environmental templates: example from Biology<br />Probability to pick up or drop certain objects is a function of local temperature<br />Temperature gradient controls location of objects<br />T<br />3.00 a.m.<br />3.00 p.m.<br />Location of Eggs, Larvae, and Pupae in the nest of the ant Acantholepis Custodiens,<br />© Guy Theraulaz<br />
  18. 18. Randomized Coverage with Collaboration<br />Translate<br />Inspect<br />Inspect<br />Avoid Obstacle<br />Wall | Robot<br />Obstacle clear<br />Search<br />Inspect<br />Mobile<br />Marker<br />pt<br />Blade<br />1-pt | Marker<br />Tt expired<br />
  19. 19. Robot Capabilities<br />Sensing: infrared distance sensors<br />Computation: FSM, wall following<br />Actuation: differential wheels<br />Communication: single bit (blade busy or not)<br />
  20. 20.
  21. 21. Improvement of Collaboration<br />Real<br />Macroscopic Model<br />
  22. 22. Example 2: Stick-Pulling<br />Goal: pull sticks out of the ground<br />Two robots need to collaborate<br />A. Martinoli, K. Easton, and W. Agassounon. Modeling Swarm Robotic Systems: A Case Study in Collaborative Distributed Manipulation. Int. Journal of Robotics Research, 23(4):415-436, 2004.<br />
  23. 23. Robotic Platform<br />16 MHz Motorola CPU<br />Incremental wheel encoders<br />6 frontal infra-red sensors<br />Position feedback in arm (communication!)<br />
  24. 24. Robot Capabilities<br />Sensing: infrared distance sensors, detect stick<br />Computation: FSM, wall following<br />Actuation: differential wheels<br />Communication: explicit, physical via stick<br />Course question: what happens if time-out is too high?<br />
  25. 25. Analysis (Intuition)<br />Time-out during wait key for performance<br />Less robots than sticks<br />Time-out too low: collaboration unlikely<br />Time-out too high: robot depletion<br />More robots than sticks<br />The longer the time-out, the better<br />Optimal value for gripping time when less robots than sticks?<br />
  26. 26. Experimental Results<br />A. Martinoli, K. Easton, and W. Agassounon. Modeling Swarm Robotic Systems: A Case Study in Collaborative Distributed Manipulation. Int. Journal of Robotics Research, 23(4):415-436, 2004.<br />
  27. 27. Example 3: Aggregation<br />Goal: aggregate objects into structures<br />Inspired by nest-building of termites<br />Algorithm<br />Search for seeds<br />Pick-up seed<br />Drop close to other seeds<br />Only seeds at end of cluster are identified as such -&gt; Line formation<br />Martinoli, A., Ijspeert, A.J. and Mondada, F. (1999) Understanding collective aggregation mechanisms: from probabilistic modelling to experiments with real robots. Robotics and Autonomous Systems, 29(1) pp. 51-63.<br />
  28. 28. Aggregation<br />Martinoli, A., Ijspeert, A.J. and Mondada, F. (1999) Understanding collective aggregation mechanisms: from probabilistic modelling to experiments with real robots. Robotics and Autonomous Systems, 29(1) pp. 51-63.<br />
  29. 29. Results<br />Martinoli, A., Ijspeert, A.J. and Mondada, F. (1999) Understanding collective aggregation mechanisms: from probabilistic modelling to experiments with real robots. Robotics and Autonomous Systems, 29(1) pp. 51-63.<br />
  30. 30. Summary<br />Reactive control: tight coupling between perception and actuation<br />Behavior is function of controller and environment<br />Collaboration in reactive swarms<br />Implicit<br />Explicit: via the environment and local communication<br />
  31. 31. Next Sessions<br />Wednesday: More on reactive algorithms<br />threshold-based algorithms<br />message propagation<br />Friday: First lab<br />

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