Multi-Robot Systems

1. Multi-Robot Systems CSCI 7000-006 Wednesday, September 9, 2009 NikolausCorrell

2. So far Reactive algorithms, robotic swarms Limited number of internal states Direct coupling between perception and action Threshold-based algorithms are a powerful method for task allocation Coordination Implicit by modifiying the environment Explicit by local communication Propagating directional information are a powerful method for navigation

3. Today Deliberative algorithms Planning vs. reacting? Computational vs. Robotic algorithms

4. Deliberation Computational representation of the problem (model, e.g. map or graph) Reasoning on representation Robot: Sensors to determine world-state Mapping algorithmic solution into action

5. “Why is robotics hard?” (again) Sensors are flaky Internal representation not accurate Planning becomes suboptimal or wrong Actuators are unreliable Mismatch between states in the plan and the world Course question: How do YOU resolve an everyday navigation problem?

6. Making robots more robust Adding sensors (allows cross-validation) But: sensors again unreliable Bayesian approach Maintain probability distribution over belief states and use sensors to update beliefs Redundancy 50% 50% 5% 95%

7. Example: Deliberation and Uncertainty Scenario: visit and circumnavigate every blade at least once Environment unknown Course question Computational representation Algorithm for complete coverage Potential problems?

8. Computational representation Graph Vertices: blades Edges: routes between blades Depth-First-Search “Count blades” Minimal spanning tree Use A* for moving toward nearest unexplored edge

9. From planning to action Sensors: distance sensor/odometer Wall following Detect round and sharp tip Controller Detect waypoint (probabilistic) Launch to neighbor (open-loop) Until blade is hit Problems Wrong waypoint Wheel-slip N. Correll, S. Rutishauser, and A. Martinoli. Comparing Coordination Schemes for Miniature Robotic Swarms: A Case Study in Boundary Coverage of Regular Structures. In The 10th International Symposium on Experimental Robotics (ISER), Rio de Janeiro, 2006. Springer Tracts in Advanced Robotics, volume 38, pages 471-480, 2008

10. Basic Navigation Behaviors 9/20/2007 Nikolaus Correll 10

11. Analysis Complete algorithm becomes probabilistic Starting over when lost Implicit collaboration 10% wheel-slip 50% wheel-slip 6000 experiments in Webots, 10% wheel-slip Time for covering one blade Probability of no navigation error

12. Debate Statement: Deterministic algorithms become probabilistic due to noise What if the sensors and controllers would be more precise? Can you find counter examples? Does planning always make sense? Why wouldn’t it?

13. Adding sensors Localization offers chance to recover Failure can be detected and robot can re-plan Problem: Noise on localization 95% successful detection

14. Algorithm Initialize new cells with p=95% Initialize unexplored neighbors with p=0% Use Dijkstra’s algorithm to move towards cell with lowest likelihood of coverage After n-th visit update cells with posterior CQ: Termination criteria? S. Rutishauser, N. Correll, and A. Martinoli. Collaborative Coverage using a Swarm of Networked Miniature Robots. Robotics & Autonomous Systems, 57(5):517-525, 2009

15. Analysis Additional sensors provide additional confidence But: Solution remains probabilistic This example: expected runtime is a function of inspected confidence

16. Explicit Collaboration Share progress via radio (broadcast) Stitch maps together using global coordinates Update coverage probabilities New Problem: communication failure 16 j i

17. 9/20/2007 Nikolaus Correll 17

18. Analysis Communication reliability impacts collaboration efficiency Communication content “deterministic” due to error correction Algorithm: non-optimal collaboration No Comm. Comm. Termination Criterion: 99% coverage

19. Summary Deliberative planning produces superior performance over reactive algorithms Both approaches are probabilistic due to real-world noise Deliberation and communication comes at cost! Find ways to recover / correct errors by adding Sensors redundancy Exchange information to strengthen hypothesises

20. State-of-the Art Example: Multi-Robot Exploration and Mapping Maps generated by laser-scan and odometry Robots drive toward unexplored frontiers Robots actively validate relative localization Relative position of team unknown initially Robots share maps Fox, D. Ko, J. Konolige, K. Limketkai, B. Schulz, D. Stewart, B. Distributed Multi-robot Exploration and Mapping. Proceedings of the IEEE, 94:7:1325-1339.

21. Map Sharing Find best match for other robot’s position given its Map Trajectory Initialize with uniform belief distribution Prune estimates as the robot moves Continue exploration together from thereon Fox, D. Ko, J. Konolige, K. Limketkai, B. Schulz, D. Stewart, B. Distributed Multi-robot Exploration and Mapping. Proceedings of the IEEE, 94:7:1325-1339.

22. Robot Coordination Trade off between exploration and exploitation Move towards unexplored frontiers Validate other robots’ positions

23. Friday No lab More deliberative algorithms: optimal coordination

24. Organization Traveling on Monday, September 14-16 Localization sensor and scanner are late Next week: Building-Week Putting together robots Commissioning laser scanner and localization system