Online optimization of modular robot locomotion

Abstract

Adaptive locomotion in unstructured and unpredictable environments is one of the most advertised features of modular robots in the literature. Autonomous modular robots are expected to adapt in the face of a dynamic environment, unexpected tasks and/or module failures. There are two levels of adaptation: within a static configuration, a chain-type modular robot can adapt its locomotion gait using its many degrees of freedom and the inherent redundancy. In addition, the robot may self-reconfigure to adapt also its morphology. Online optimization of locomotion in a self-organizing manner is mandatory within this context. The contribution of this paper is three-fold: i) Inspired by central pattern generators (CPGs) found in vertebrates, we propose a distributed locomotion controller based on coupled nonlinear oscillators; ii) For offline optimization, a genetic algorithm that co-evolves the CPG with the configuration of the modular robot is presented; iii) The ultimate goal of our research being autonomous locomotion, the focus of the paper lies on a novel, fast online adaptation method for coupled nonlinear oscillators. The algorithm allows fast online optimization (adaptation) of locomotion gaits in the face of module failures or new, previously unknown configurations. A realistic simulation of our hardware prototype YaMoR is used for the experiments.