Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm - Tao, Chongben; Xue, Jie; Zhang, Zufeng; Cao, Feng; Li, Chunguang; Gao, Hanwen (Frontiers in Neurorobotics, 2021)
The speed, stability, and flexibility of humanoid robot walking in RoboCup3D competition are key factors. In this paper, a gait optimization method based on Parallel Comprehensive Learning Particle Swarm Optimizer (PCLPSO) is proposed. Firstly, the key parameters affecting the walking gait of humanoid robot are selected based on natural Zero Moment Point (ZMP) trajectory planning method. Secondly, a parallel and distributed multi robot gait training environment based on RoboCup3D is built, and PCLPSO algorithm is applied to automatically optimize the walking speed and stability of humanoid robot. Finally, a layered learning approach is used to optimize the turning ability of humanoid robot. The experimental results show that PCLPSO algorithm achieves quickly optimal solution, and the humanoid robot optimized possesses a fast and steady gait and excellent turnaround.