Multi-objective parameter CPG optimization for gait generation of a biped robot

This paper presents a biped gait optimization system that combines bio-inspired Central Patterns Generators (CPGs) and a multi-objective evolutionary algorithm. CPGs are modeled as autonomous differential equations, that generate the necessary limb movements to perform the walking gait of a biped robot. The search for the best set of CPG parameters is optimized by considering multiple objectives and according to a staged evolution. A sensitivity analysis is used to evaluate the relationship between objectives, objectives and parameters, and allows to determine the functional meanings of the parameters. This resulting functional analysis enables to verify which parameters are relevant to the motor behaviors. The simulation results show the effectiveness of the proposed approach. The different obtained walking gait solutions correspond to different trade-offs between the objectives.