Morphology Transformation of Underwater Self-Reconfigurable Modular Robots via Heterogeneous Decomposition and Distributed Control

Abstract

<p>This paper addresses the morphology transformation problem of an underwater self-reconfigurable modular robotic system. Morphology decomposition and reconnections are reduced to mitigate transformation failures and the overhead of underwater wireless communication, giving rise to subgraph matching problems. We propose an efficient probabilistic decomposition method by constraining the search depth of maximal common subgraphs of the initial and goal morphologies. The computational complexity reduces from O(n2) to O(n). The decomposition yields a swarm of heterogeneous clusters, which are interconnected modular robots of varying quantities. The heterogeneity makes the exchange of clusters' designated positions in the goal morphology not immediately feasible. Subsequently, we present Distributed Control with minimal In-situ task Refinement (DCIR). DCIR is proven to ensure collision-free and deadlock-free morphology transformation. The numerical simulations involving up to 641 modular robots and experiments on 6 robots have shown that DCIR scales well with the number of modular robots, runs in real time, and reduces traveling distances by at least 14% and communication costs by about half, compared to the distributed control with homogeneous task exchange and the modified surface sliding method.</p>