Natural Growth-Inspired Distributed Self-Reconfiguration of UBot Robots

Abstract

The decentralized self-reconfiguration of modular robots has been a challenging problem. This work proposed a biological method inspired by the plant growth for the distributed self-reconfiguration of UBot systems. L-systems are implemented to construct target topology, and turtle interpretation is extended to lead the self-reconfiguration process. Parametric reproduction rules introduce the external influence to the reconfiguration process by distributed modules’ local sensing. Each module can move independently to change relative positions, and robotic structures develop in the natural growth style. This leads to a convergent and environmentally sensitive control method for the distributed self-reconfiguration. Reconfiguration processes can converge to desired configuration and are scalable to module numbers by reproducing predefined substructures in principle. The overall performance of the proposed strategy is evaluated with simulations and 11 experiments. Simulation and experimental results turn out to be convergent and environmentally sensitive.