Adaptive gait generation for humanoid robot using evolutionary neural model optimized with modified differential evolution technique

Gait generation plays a decisive role to the performance of biped robot walking. Under mathematical viewpoint, the task of gait generation design is investigated as an optimization problem with respect to various trade-off constraints, hence it prefers to evolutionary computation techniques. This paper introduces a novel approach for the biped robot gait generation which aims to enable humanoid robot to walk more naturally and stably on flat platform. The proposed modified differential evolution (MDE) optimisation algorithm is initiatively applied to optimally identify the novel adaptive evolutionary neural model (AENM) for a dynamic biped gait generator. The performance of proposed MDE method is demonstrated in comparison with other genetic algorithm (GA) and particle swarm optimisation (PSO) approaches. The proposed method is implemented and tested on a prototype small-sized humanoid robot. The identification result demonstrates that the new proposed neural AENM model proves an effective approach for a robust and precise biped gait generation.