Multi-robot Systems

1. Multi-Robot Systems CSCI 7000-006 Monday, August 31, 2009 NikolausCorrell

2. So far Introduction to robotics and multi-robot systems Similar algorithms and properties for robot teams, robot swarms and smart materials

3. Today Reactive algorithms Environmental templates Collaboration in reactive swarms

4. Reactive Algorithms Directly couple perception to action Extremely simple hardware (analog electronics will do) Robustness out of simplicity Potential for miniaturization First instance: Grey Walter’s tortoises © The i-Swarm project

5. Concept: Braitenberg Vehicles Couple perception to action Sensor input coupled to actuator output Inspired by brain architecture left/right hemisphere Neural network Course question: how do the vehicles behave with respect to a light source? Light Sensor Motors

6. More complex behaviors Braitenberg More sensors (e.g. camera) More connections (e.g. brain) Synthesis by genetic algorithms Modify random connections Unfit individuals fall of the table Hierarchical Decompositon

7. Subsumption Architecture (Brooks) Decompose behavior into modules Collision avoidance, light following, etc. Arrange modules in layers representing goals Upper layers subsume lower layers Difficult to design with increasing complexity Explore world Wander around Avoid Obstacles Brooks, R. (1986). "A robust layered control system for a mobile robot". Robotics and Automation, IEEE Journal of 2 (1): 14–23.

8. Alternative view: Artificial Potential Fields Aka virtual physics, motor schemes Goals are represented by virtual forces (attraction/repulsion) Forces are calculated from sensor input Addition yields vector field that the robots follow Obvious problem: local minima and cycles © Craig Reynolds

9. Further Reading ValentionBraitenberg“Experiments in synthetic psychology”, 1986 Rodney Brooks“Elephants don’t play chess”, 1990 Ronald Arkin“Behavior-based Robotics”, 1998

10. Example: Jet Turbine Inspection Goal: surround every blade in a turbine with a robotic sensor Robots need to be small, only local communication Alice(ASL, EPFL), sugar cube, 368bytes of RAM

11. Robotic Platform Alice miniature robot [Caprari2005] PIC microcontroller (368 bytes RAM, 8Kb FLASH) Length of 22mm Maximal speed of 4cm/s, stepper motors 4 IR modules serve as very crude proximity sensors (3cm) and local communication devices Energetic autonomy 5h-10h

12. Baseline: Randomized Coverage without Localization Search Inspect Translate Avoid Obstacle Wall | Robot Obstacle clear Search Inspect Translate along blade pt Blade 1-pt Tt expired

13. Robot Capabilities Sensing: infrared distance sensors Computation: FSM, wall following Actuation: differential wheels Communication: none

14. Analysis (Intuition) Collaboration: implicit Completeness: probabilistic, asymptotic Probability to leave blade at round or sharp tip affects robot distribution

15. Experimental Results 20, 25, 30 robots

16. Spatial distribution for pt=0 Leaving the blades at a tip generates drift in the environment “Enviromental Template” Probability to inspect some of the blades higher

17. Exploiting environmental templates: example from Biology Probability to pick up or drop certain objects is a function of local temperature Temperature gradient controls location of objects T 3.00 a.m. 3.00 p.m. Location of Eggs, Larvae, and Pupae in the nest of the ant Acantholepis Custodiens, © Guy Theraulaz

18. Randomized Coverage with Collaboration Translate Inspect Inspect Avoid Obstacle Wall | Robot Obstacle clear Search Inspect Mobile Marker pt Blade 1-pt | Marker Tt expired

19. Robot Capabilities Sensing: infrared distance sensors Computation: FSM, wall following Actuation: differential wheels Communication: single bit (blade busy or not)

21. Improvement of Collaboration Real Macroscopic Model

22. Example 2: Stick-Pulling Goal: pull sticks out of the ground Two robots need to collaborate 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.

23. Robotic Platform 16 MHz Motorola CPU Incremental wheel encoders 6 frontal infra-red sensors Position feedback in arm (communication!)

24. Robot Capabilities Sensing: infrared distance sensors, detect stick Computation: FSM, wall following Actuation: differential wheels Communication: explicit, physical via stick Course question: what happens if time-out is too high?

25. Analysis (Intuition) Time-out during wait key for performance Less robots than sticks Time-out too low: collaboration unlikely Time-out too high: robot depletion More robots than sticks The longer the time-out, the better Optimal value for gripping time when less robots than sticks?

26. Experimental Results 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.

27. Example 3: Aggregation Goal: aggregate objects into structures Inspired by nest-building of termites Algorithm Search for seeds Pick-up seed Drop close to other seeds Only seeds at end of cluster are identified as such -> Line formation 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.

28. Aggregation 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.

29. Results 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.

30. Summary Reactive control: tight coupling between perception and actuation Behavior is function of controller and environment Collaboration in reactive swarms Implicit Explicit: via the environment and local communication

31. Next Sessions Wednesday: More on reactive algorithms threshold-based algorithms message propagation Friday: First lab