Dynamic reconfiguration in modular robots using graph partitioning-based coalitions

We consider the problem of dynamic self-reconfiguration in a modular self-reconfigurable robot (MSR). Previous approaches to MSR self-reconfiguration solve this problem using algorithms that search for a goal configuration in the MSR’s configuration space. In contrast, we model the selfreconfiguration problem as a constrained optimization problem that attempts to minimize the reconfiguration cost while achieving a desirable configuration. We formulate the MSR self-reconfiguration problem as finding the optimal coalition structure within a coalition game theoretic framework. To reduce the complexity of finding the optimal coalition structure, we represent the set of all robot modules as a fullyconnected graph. Each robot module corresponds to a vertex of the graph and edge weights represent the utility of a pair of modules being in the same coalition (or, connected component). The value of a coalition structure is then defined as the sum of the weights of all edges that are completely within the same coalition in that coalition structure. We then use a graph partitioning technique to cluster the vertices (robot modules) in the constructed graph so that the obtained coalition structure has close to optimal value. The clustering algorithm has time complexity polynomial in the number of agents, n, and yields an O(log n) approximation. We have verified our technique experimentally for a variety of settings. Our results show that the graph clusteringbased self-reconfiguration algorithm performs comparably with two other existing algorithms for determining optimal coalition structures. 1