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Cognitive Colonization

Cognitive Colonization. The Robotics Institute Carnegie Mellon University. Dean Boustead, Bernardine Dias, Bruce Digney, Martial Hebert, Bart Nabbe, Tony Stentz, Charlie Smart, Scott Thayer, Rob Zlot. Schedule. Impact. Robust Colonization. Port to Military Platforms.

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Cognitive Colonization

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  1. Cognitive Colonization The Robotics Institute Carnegie Mellon University Dean Boustead, Bernardine Dias, Bruce Digney, Martial Hebert, Bart Nabbe, Tony Stentz, Charlie Smart, Scott Thayer, Rob Zlot

  2. Schedule Impact Robust Colonization Port to Military Platforms • • Agile, robust multi-robot systems • • Opportunistic distributed/centralized • control • • Sensing scales to mission parameters • • “Fire-and-Forget” mission capability Colonization Dynamics 2001 2002 2003 2000 Cognitive Colonization New Ideas • Free market-based distributed control • Specialization through functional roles • Cooperative planning and perception • Task-level autonomy for robot colonies Static Colonization

  3. Presentation Outline • Requirements • Software Architecture • Multiple Roles • Cooperative Stereo • Robot Improvements • Dynamic Capabilities • Experimental Results • Status and Future Work

  4. Requirements • is robust to individual robot failure; • does not depend on reliable communications; • can perform global tasks given the limited sensing and computational capabilities of individual robots; • learn to perform better through experience. Distributed robotics for small-scale mobile robots calls for a software system that:

  5. Free Market Architecture • Robots in a team are organized as an economy • Team mission is best achieved when the economy maximizes production and minimizes costs • Robots interact with each other to exchange money for tasks to maximize profit • Robots are both self-interested and benevolent, since it is in their self interest to do global good

  6. Distributed Mapping Example Operator Exec Tasks performed --> <-- Revenue paid

  7. Distributed Mapping Roles Reserve Robot Mapping Squad Leader Squad Communications Relay Robot Unattached Robot Mapping Robot

  8. Multiple Roles in Simulation Cost • Cost switched from distance based to time based. • Leader and communication roles introduced in addition to sensing role. Time R2 R1

  9. Initial Time-Optimized Plan

  10. Robot Negotiation with Leader

  11. Optimization Via Comms

  12. Robot Improvements I • Improved robot dead reckoning capability by adding gyros. • Added “unachievable goal” detection with re-assignment of tasks to other robots. • Rapid deployment through formations

  13. Robot Improvements II • Added “dead robot” detection with re-assignment of tasks to other robots. • Added “bump sensing” to cover for sonar misses: a “bump” puts obstacle in navigation map.

  14. New Dynamic Capabilities • Ported inter-robot negotiation from simulator to real robot test bed to allow for further optimization in response to new tasks, new robots, unexpected results, or lost assets. • Added dynamic goal creation to map an unknown area: four schemes were implemented.

  15. Dynamic Goal Creation Schemes • Greedy: robot creates new goals in unexplored areas near its present location. • Quad-tree: robot creates new goals at center of quad-tree nodes describing unexplored space. • Regular: robot creates new goals in regular pattern over unexplored area. • Random: robot creates random new goals in unexplored area.

  16. Experimental Results • Preliminary results indicate that the random strategy works best because it disperses the robots. • Regardless of the scheme employed, the robots further optimize it by exchanging goals through the negotiation process.

  17. Interior Mapping Video: 4 robots

  18. Interior Mapping Trace

  19. Final Interior Map 30 m 45 m

  20. GUI Map of Interior Environment

  21. Final Exterior Map

  22. GUI of Exterior Environment

  23. Tulip Grove: 5 robots

  24. Cooperative stereo • Configuration: • Wide reconfigerable baseline • Uncalibrated baseline • Techniques: • Robust epipolar estimation • Planar homographies

  25. Point correspondences • Feature seeding • Robust matching (one to many) • Global consistent matching (one to one) • Compute epipolar geometry

  26. Planar correspondences • Line seeding, 4 cycle generation • Planar matching • Compute homography • Warp plane • Compute correlation • Global consistent matching • Reconstruct epipolar geometry

  27. Reconstructed geometry

  28. Current Status • Multiple roles tested in simulation. • Full control architecture ported to robots, complete with inter-robot negotiation and robustness to sensing, navigation, and hardware faults. • Distributed mapping demonstrated in unknown environment (interior and exterior) using multiple robots.

  29. Next Steps • Switch robots from distance to time cost regime. • Enable robots to sell map information to each other to improve estimates of navigation costs. • Fuse sonar mapping with stereo mapping to produce human-observable maps of an unknown environment.

  30. Technology Transfer • DRES: • NASA: • ARL Robotics CTA:

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