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searchUCSG: a fast coalition structure search algorithm for modular robot reconfiguration under uncertainty

Published online by Cambridge University Press:  13 February 2014

Ayan Dutta ,
Prithviraj Dasgupta ,
José Baca  and
Carl Nelson
Ayan Dutta*
Affiliation:
Computer Science Department, University of Nebraska at Omaha, Omaha, NE 68182, USA
Prithviraj Dasgupta
Affiliation:
Computer Science Department, University of Nebraska at Omaha, Omaha, NE 68182, USA
José Baca
Affiliation:
Computer Science Department, University of Nebraska at Omaha, Omaha, NE 68182, USA
Carl Nelson
Affiliation:
Mechanical and Materials Engineering Department, University of Nebraska-Lincoln, Lincoln, NE 68508, USA
*
*Corresponding author. E-mail: [email protected], [email protected]
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Summary

We consider the problem of dynamic reconfiguration by modular self-reconfigurable robots (MSRs) in the presence of uncertainty in their motion and the environment. Specifically, we consider the situation where the MSR is unable to continue its motion in its current configuration and needs to identify a new configuration among the existing modules, which would be the most configuration suitable for performing the robot's assigned task under the current circumstances. To address this problem, we propose a new data structure called an uncertain coalition structure graph (UCSG) that accommodates uncertainty in the MSR's motion and the environment, using a framework from cooperative game theory called the coalition structure graph. We then propose a new search algorithm called searchUCSG that intelligently prunes nodes from the UCSG using a modified branch-and-bound technique. We have shown analytically that our algorithm is anytime, that is, if it terminates arbitrarily, it returns the best solution found thus far, which is guaranteed to be within a constant bound from the optimal solution. We have verified the performance of our algorithm experimentally in simulation and shown that it is able to find a solution that is within the worst bound of 80% of the optimal solution while exploring only half of the nodes in the UCSG. Our algorithm also takes lesser computation time than the existing algorithms (that do not model uncertainty) for solving similar problems. Finally, to verify the operation of our algorithm, we have implemented it to partition a set of mobile e-puck robots into clusters and shown how different number of robots and different robot motion uncertainty parameters affect the formed clusters.

Keywords

DARS2012Modular robotsMulti-robot systemsSpace roboticsAutomation
Type
Articles
Information
Robotica , Volume 32 , Issue 2: Distributed Autonomous Robotic Systems (DARS). , March 2014 , pp. 225 - 244
Copyright
Copyright © Cambridge University Press 2014 

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