We consider the problem of reconfiguration planning in modular robots. Current techniques for reconfiguration planning usually specify the destination configuration for a modular robot explicitly. We posit that in uncertain environments the desirable configuration for a modular robot is not known beforehand and has to be determined dynamically. In this paper, we consider this problem of how to identify a new 'best' configuration when a modular robot is unable to continue operating efficiently in its current configuration. We build on a technique that enumerates all the possible partitions of a set of modules requiring reconfiguring as a coalition structure graph (CSG) and finds the 'best' node in that graph. We propose a new data structure called an uncertain CSG (UCSG) that augments the CSG to handle uncertainty originating from the motion and performance of the robot. We then propose a new search algorithm called searchUCSG that intelligently prunes nodes from the UCSG using a modified branch and bound technique. Experimental results show that our algorithm is able to find a node that is within a worst bound of 80% of the optimal or best node in the UCSG while exploring only half the nodes in the UCSG. The time taken by our algorithm in terms of the number of nodes explored is also consistently lower than existing algorithms (that do not model uncertainty) for searching a CSG.
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