A Multi-Objective Evolutionary Algorithm for Neuro-Locomotion of a Legged Robot With Malfunction Compensation

Abstract

Dynamic quadruped locomotion implies high-intensity integration toward environmental factors and requires considering the information from sensory feedback. The authors represent CPG-based locomotion model with sensorimotor coordination. They build an efficient integration between motor and sensory neurons that can generate dynamic behavior, especially in locomotion coordination during leg malfunction. They emphasize an optimization strategy to optimize the interconnection structure of CPG-based locomotion model. They use a multi-objective evolutionary algorithm to optimize the synaptic weight between motor–motor neurons and motor–sensory neurons. The applied cascade optimization is 1) dynamic gait pattern optimization using desired speed and torso oscillation as the fitness function and 2) malfunction compensation optimization using moving direction error and torso oscillation as the fitness evaluation. The proposed model has been applied to simulated and real middle-size quadruped robots. It showed the proposed optimization can generate a smooth transition during a robot's leg unction.