Cognitive Colonization

The Robotics Institute

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


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


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


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:


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


Distributed Mapping Example

Operator Exec

<-- Revenue paid

Tasks performed -->


Distributed Mapping Roles

Unattached Robot

ReserveRobot

Leader Squad

Mapping Squad

MappingRobot

CommunicationsRelay Robot


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


Initial Time-Optimized Plan


Robot Negotiation with Leader


Optimization Via Comms


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


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.


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.


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.


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.


Interior Mapping Video: 4 robots


Interior Mapping Trace


Final Interior Map

45 m

30 m


GUI Map of Interior Environment


Final Exterior Map


GUI of Exterior Environment


Tulip Grove: 5 robots


Cooperative stereo Configuration:

Wide reconfigerable baseline Uncalibrated baseline

Techniques: Robust epipolar estimation Planar homographies


Point correspondences

•Feature seeding•Robust matching (one to many)•Global consistent matching (one to one)•Compute epipolar geometry


Planar correspondences

•Line seeding, 4 cycle generation•Planar matching

•Compute homography•Warp plane•Compute correlation

•Global consistent matching•Reconstruct epipolar geometry


Reconstructed geometry


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.


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.


Technology Transfer DRES:

NASA:

ARL Robotics CTA:

Cognitive Colonization

Documents

Transcript of Cognitive Colonization