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# September 9, Deliberative Algorithms I

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Multi-Robot Systems

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• I will now demonstrate you the modeling methodology using the application that has motivated my PhD thesis work. The scenario is jet turbine inspection, that is releasing a swarm of robots into a piece of machinery for inspecting its inside.I abstracted this task to a 2D environment where robots need to circumnavigate a set of blades, which can be casted as a generic graph coverage problem.I am interested to model a system as a function of its algorithm and its resources using a common method. I therefore implemented a series of algorithms ranging from very simple reactive ones to NP hard deliberative algorithms. I consider two different scenarios, with and without localization and robots with different capabilities. At the top right you see an Alice robot that has been developed at EPFL. The robot fits into a square of 2 by 2 cm and is extremely simple: it has a small microcontroller, watch motors and can communicate up to 3cm using infrared. For running all the algorithms I was interested in, I equipped the robot with two additional modules. A radio module running TinyOS and a 30 by 30 pixel color camera. The camera is actually used for localization and together with the radio, I can transmit images back to a base station.
• ### September 9, Deliberative Algorithms I

1. 1. Multi-Robot Systems<br />CSCI 7000-006<br />Wednesday, September 9, 2009<br />NikolausCorrell<br />
2. 2. So far<br />Reactive algorithms, robotic swarms<br />Limited number of internal states<br />Direct coupling between perception and action<br />Threshold-based algorithms are a powerful method for task allocation<br />Coordination<br />Implicit by modifiying the environment<br />Explicit by local communication<br />Propagating directional information are a powerful method for navigation<br />
3. 3. Today<br />Deliberative algorithms<br />Planning vs. reacting?<br />Computational vs. Robotic algorithms<br />
4. 4. Deliberation<br />Computational representation of the problem (model, e.g. map or graph)<br />Reasoning on representation<br />Robot:<br />Sensors to determine world-state<br />Mapping algorithmic solution into action<br />
5. 5. “Why is robotics hard?” (again)<br />Sensors are flaky<br />Internal representation not accurate<br />Planning becomes suboptimal or wrong<br />Actuators are unreliable<br />Mismatch between states in the plan and the world<br />Course question: How do YOU resolve an everyday navigation problem?<br />
6. 6. Making robots more robust<br />Adding sensors (allows cross-validation)<br />But: sensors again unreliable<br />Bayesian approach<br />Maintain probability distribution over belief states and use sensors to update beliefs<br />Redundancy<br />50%<br />50%<br />5%<br />95%<br />
7. 7. Example: Deliberation and Uncertainty<br />Scenario: visit and circumnavigate every blade at least once<br />Environment unknown<br />Course question<br />Computational representation<br />Algorithm for complete coverage<br />Potential problems?<br />
8. 8. Computational representation<br />Graph<br />Vertices: blades<br />Edges: routes between blades <br />Depth-First-Search<br />“Count blades”<br />Minimal spanning tree<br />Use A* for moving toward nearest unexplored edge<br />
9. 9. From planning to action<br />Sensors: distance sensor/odometer<br />Wall following<br />Detect round and sharp tip<br />Controller<br />Detect waypoint (probabilistic)<br />Launch to neighbor (open-loop)<br />Until blade is hit<br />Problems<br />Wrong waypoint<br />Wheel-slip<br />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<br />
10. 10. Basic Navigation Behaviors<br />9/20/2007<br />Nikolaus Correll<br />10<br />
11. 11. Analysis<br />Complete algorithm becomes probabilistic<br />Starting over when lost<br />Implicit collaboration<br />10% wheel-slip<br />50% wheel-slip<br />6000 experiments in Webots, 10% wheel-slip<br />Time for covering one blade<br />Probability of no navigation error<br />
12. 12. Debate<br />Statement: Deterministic algorithms become probabilistic due to noise<br />What if the sensors and controllers would be more precise? Can you find counter examples?<br />Does planning always make sense? Why wouldn’t it?<br />
13. 13. Adding sensors<br />Localization offers chance to recover<br />Failure can be detected and robot can re-plan<br />Problem: Noise on localization<br />95% successful detection<br />
14. 14. Algorithm<br />Initialize new cells with p=95%<br />Initialize unexplored neighbors with p=0%<br />Use Dijkstra’s algorithm to move towards cell with lowest likelihood of coverage<br />After n-th visit update cells with posterior<br />CQ: Termination criteria?<br />S. Rutishauser, N. Correll, and A. Martinoli. Collaborative Coverage using a Swarm of Networked Miniature Robots. Robotics & Autonomous Systems, 57(5):517-525, 2009<br />
15. 15. Analysis<br />Additional sensors provide additional confidence<br />But: Solution remains probabilistic<br />This example: expected runtime is a function of inspected confidence<br />
16. 16. Explicit Collaboration<br />Share progress via radio (broadcast)<br />Stitch maps together using global coordinates<br />Update coverage probabilities<br />New Problem: communication failure<br />16<br />j<br />i<br />
17. 17. 9/20/2007<br />Nikolaus Correll<br />17<br />
18. 18. Analysis<br />Communication reliability impacts collaboration efficiency<br />Communication content “deterministic” due to error correction<br />Algorithm: non-optimal collaboration<br />No Comm.<br />Comm.<br />Termination Criterion: 99% coverage<br />
19. 19. Summary<br />Deliberative planning produces superior performance over reactive algorithms<br />Both approaches are probabilistic due to real-world noise<br />Deliberation and communication comes at cost!<br />Find ways to recover / correct errors by adding<br />Sensors<br />redundancy<br />Exchange information to strengthen hypothesises<br />
20. 20. State-of-the Art Example: Multi-Robot Exploration and Mapping<br />Maps generated by laser-scan and odometry<br />Robots drive toward unexplored frontiers<br />Robots actively validate relative localization<br />Relative position of team unknown initially<br />Robots share maps<br />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.<br />
21. 21. Map Sharing<br />Find best match for other robot’s position given its<br />Map<br />Trajectory<br />Initialize with uniform belief distribution<br />Prune estimates as the robot moves<br />Continue exploration together from thereon<br />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.<br />
22. 22. Robot Coordination<br />Trade off between exploration and exploitation<br />Move towards unexplored frontiers<br />Validate other robots’ positions<br />
23. 23. Friday<br />No lab<br />More deliberative algorithms: optimal coordination<br />
24. 24. Organization<br />Traveling on Monday, September 14-16<br />Localization sensor and scanner are late<br />Next week: Building-Week<br />Putting together robots<br />Commissioning laser scanner and localization system<br />