This document discusses reactive algorithms for multi-robot systems. Reactive algorithms directly couple perception to action using simple hardware. Examples of reactive algorithms discussed include Braitenberg vehicles and Brooks' subsumption architecture. Collaboration in reactive swarms can occur implicitly through environmental templates, where robots' probabilities of certain behaviors are functions of local environmental conditions, or explicitly through local communication between robots. Examples demonstrated include randomized coverage of turbine blades, stick pulling between two robots, and collective aggregation of objects.

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

2. So farIntroduction to robotics and multi-robot systemsSimilar algorithms and properties for robot teams, robot swarms and smart materials

3. TodayReactive algorithmsEnvironmental templatesCollaboration in reactive swarms

4. Reactive AlgorithmsDirectly couple perception to actionExtremely simple hardware (analog electronics will do)Robustness out of simplicityPotential for miniaturizationFirst instance: Grey Walter’s tortoises© The i-Swarm project

5. Concept: Braitenberg VehiclesCouple perception to actionSensor input coupled to actuator outputInspired by brain architecture left/right hemisphereNeural networkCourse question: how do the vehicles behave with respect to a light source?Light SensorMotors

6. More complex behaviorsBraitenbergMore sensors (e.g. camera)More connections (e.g. brain)Synthesis by genetic algorithmsModify random connectionsUnfit individuals fall of the tableHierarchical Decompositon

7. Subsumption Architecture (Brooks)Decompose behavior into modulesCollision avoidance, light following, etc.Arrange modules in layers representing goalsUpper layers subsume lower layersDifficult to design with increasing complexityExplore worldWander aroundAvoid ObstaclesBrooks, 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 FieldsAka virtual physics, motor schemesGoals are represented by virtual forces (attraction/repulsion) Forces are calculated from sensor inputAddition yields vector field that the robots followObvious problem: local minima and cycles© Craig Reynolds

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

10. Example: Jet Turbine InspectionGoal: surround every blade in a turbine with a robotic sensorRobots need to be small, only local communicationAlice(ASL, EPFL), sugar cube, 368bytes of RAM

11. Robotic PlatformAlice miniature robot [Caprari2005]PIC microcontroller (368 bytes RAM, 8Kb FLASH)Length of 22mmMaximal speed of 4cm/s, stepper motors4 IR modules serve as very crude proximity sensors (3cm) and local communication devices Energetic autonomy 5h-10h

12. Baseline: Randomized Coverage without LocalizationSearchInspectTranslateAvoid ObstacleWall | RobotObstacle clearSearchInspectTranslatealong bladeptBlade1-ptTt expired

13. Robot CapabilitiesSensing: infrared distance sensorsComputation: FSM, wall followingActuation: differential wheelsCommunication: none

14. Analysis (Intuition)Collaboration: implicitCompleteness: probabilistic, asymptoticProbability to leave blade at round or sharp tip affects robot distribution

15. Experimental Results20, 25, 30 robots

16. Spatial distribution for pt=0Leaving 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 BiologyProbability to pick up or drop certain objects is a function of local temperatureTemperature gradient controls location of objectsT3.00 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 CollaborationTranslateInspectInspectAvoid ObstacleWall | RobotObstacle clearSearchInspectMobileMarkerptBlade1-pt | MarkerTt expired

19. Robot CapabilitiesSensing: infrared distance sensorsComputation: FSM, wall followingActuation: differential wheelsCommunication: single bit (blade busy or not)

21. Improvement of CollaborationRealMacroscopic Model

22. Example 2: Stick-PullingGoal: pull sticks out of the groundTwo robots need to collaborateA. 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 Platform16 MHz Motorola CPUIncremental wheel encoders6 frontal infra-red sensorsPosition feedback in arm (communication!)

24. Robot CapabilitiesSensing: infrared distance sensors, detect stickComputation: FSM, wall followingActuation: differential wheelsCommunication: explicit, physical via stickCourse question: what happens if time-out is too high?

25. Analysis (Intuition)Time-out during wait key for performanceLess robots than sticksTime-out too low: collaboration unlikelyTime-out too high: robot depletionMore robots than sticksThe longer the time-out, the betterOptimal value for gripping time when less robots than sticks?

26. Experimental ResultsA. 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: AggregationGoal: aggregate objects into structuresInspired by nest-building of termitesAlgorithmSearch for seedsPick-up seedDrop close to other seedsOnly seeds at end of cluster are identified as such -> Line formationMartinoli, 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. AggregationMartinoli, 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. ResultsMartinoli, 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. SummaryReactive control: tight coupling between perception and actuationBehavior is function of controller and environmentCollaboration in reactive swarmsImplicitExplicit: via the environment and local communication

31. Next SessionsWednesday: More on reactive algorithmsthreshold-based algorithmsmessage propagationFriday: First lab