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September 9, Deliberative Algorithms I

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Multi-Robot Systems

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.
  • Transcript

    • 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