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Cognitive ColonizationThe Robotics Institute
Carnegie Mellon University
Dean Boustead, Bernardine Dias, Bruce Digney, Martial Hebert, Bart Nabbe, Tony Stentz, Charlie
Smart, Scott Thayer, Rob Zlot
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Cognitive ColonizationNew Ideas
• Free market-based distributed control
• Specialization through functional roles
• Cooperative planning and perception
• Task-level autonomy for robot colonies
Impact• Agile, robust multi-robot systems
• Opportunistic distributed/centralized control
• Sensing scales to mission parameters
• “Fire-and-Forget” mission capability
ScheduleRobust
ColonizationPort to Military
Platforms
ColonizationDynamics
20002000 20012001
20022002
20032003
Static Colonization
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Presentation Outline Requirements Software Architecture Multiple Roles Cooperative Stereo Robot Improvements Dynamic Capabilities Experimental Results Status and Future Work
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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 robotscalls for a software system that:
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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
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Distributed Mapping Example
Operator Exec
<-- Revenue paid
Tasks performed -->
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Distributed Mapping Roles
Unattached Robot
ReserveRobot
Leader Squad
Mapping Squad
MappingRobot
CommunicationsRelay Robot
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Multiple Roles in Simulation
Cost switched from distance based to time based.
Leader and communication roles introduced in addition to sensing role.
Time
Cost
R1
R2
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Initial Time-Optimized Plan
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Robot Negotiation with Leader
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Optimization Via Comms
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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
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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.
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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.
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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.
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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.
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Interior Mapping Video: 4 robots
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Interior Mapping Trace
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Final Interior Map
45 m
30 m
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GUI Map of Interior Environment
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Final Exterior Map
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GUI of Exterior Environment
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Tulip Grove: 5 robots
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Cooperative stereo Configuration:
Wide reconfigerable baseline Uncalibrated baseline
Techniques: Robust epipolar estimation Planar homographies
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Point correspondences
•Feature seeding•Robust matching (one to many)•Global consistent matching (one to one)•Compute epipolar geometry
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Planar correspondences
•Line seeding, 4 cycle generation•Planar matching
•Compute homography•Warp plane•Compute correlation
•Global consistent matching•Reconstruct epipolar geometry
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Reconstructed geometry
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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.
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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.
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Technology Transfer DRES:
NASA:
ARL Robotics CTA:
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