Multi-Robot Systems

1. Multi-Robot Systems CSCI 7000-006 Wednesday, August 27, 2009 NikolausCorrell

2. Last lecture Highlights from mechanisms control algorithms coordination Multi-robot system Kiva Centralized control Task allocation on grid environment

3. Last Lecture Possible algorithms are a function of available Sensing Computation Communication Mechanism

4. Today Why Multi-Robot Systems? Planning and Coordination Reactive vs. Deliberative Algorithms

5. Course Question When and why would it make sense to actually use more than one robot?

6. Why Multi-Robot Systems? Robustness “If one robot fails, the others step in” Scalability “If the problem gets bigger, just get more robots” Performance “More robots will get this done faster” Specialization “While some robots do this, others do already that”

7. Reactive Coordination: Shortest path routing in ant colonies Task: find shortest path Ants choose bridge probabilistically Ants leave pheromone trace Probability function of pheromone level Pheromones evaporate eventually Jean-Louis Deneubourg, ULB

8. Analysis Sensing: pheromone level Computation: biased random number generator (or just noise when reading pheromones?) Actuation:biased random walk Communication: indirect

9. Course Questions Come up with “better” algorithms for solving the problem using a robot swarm. What capabilities would the robots need for your solution? Come up with an algorithm that requires a single robot. What sensors does it need?

11. Concept “Shortest Path”

12. Reliable ExecutionSouth

13. Alternative 2: Single robot G S

14. Lessons learned from the ants Robustness Unreliable team members Misreading of the pheromone trail Scalability Yes, due to decentralized, distributed coordination Performance Probabilistic completeness Specialization Not in this example (more on ants later) Good performance despite limited sensing, computation, and communication

15. Deliberative Coordination: Yacht Racing

16. System architecture Weather Sails Grinders Strategist Navigator Helmsman Tactician Runner Trim Communication Competition Trimmers Sensing Landmarks/Position Computation Actuation

17. Analysis Sensing:weather, competition, landmarks Computation: optimal policies for heading, sails and trim Actuation:heading, sails and trim Communication: voice and gestures, potentially lossy

18. Lessons from yachting example Robustness Not robust to communication and material failures Scalability Limited due to hierarchical, centralized architecture Performance Optimal given optimal sensing, communication and actuation Specialization high Fortune favors the bold: “Best” policies yield close to optimal performance under uncertainty.

19. Course question Could the boat be run by a single person?

20. Multi-Robot vs. Single Robot Systems Each multi-robot system can be replaced by a single robot The real question is: what is feasible? The number of robots to solve a given task is a resource trade-off problem: Few more capable units vs. many simple ones What are the constraints on time/cost/size to solve the problem …

21. Course Question Look at the diagram Where would you position the ants? Where would you position the yacht crew? Degree of Planning Degree of Coordination

22. Summary A multi-robot system is determined by the distribution of Sensing Computation Actuation Communication A coordination algorithm is a best-effort approach based on these capabilities Best possible planning Best possible coordination Capabilities are almost always probabilistic and make coordination a hard problem

23. Next Lectures Friday: Components of the Buff-Bot Next week Lecture: a case study in multi-robot inspection Practice: robotic operating systems Lab: getting started with ROS