Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm

To improve the fast and stable walking ability of a humanoid robot, this paper proposes a gait optimization method based on a parallel comprehensive learning particle swarm optimizer (PCLPSO). Firstly, the key parameters affecting the walking gait of the humanoid robot are selected based on the natural zero-moment point trajectory planning method. Secondly, by changing the slave group structure of the PCLPSO algorithm, the gait training task is decomposed, and a parallel distributed multi-robot gait training environment based on RoboCup3D is built to automatically optimize the speed and stability of bipedal robot walking. Finally, a layered learning approach is used to optimize the turning ability of the humanoid robot. The experimental results show that the PCLPSO algorithm achieves a quickly optimal solution, and the humanoid robot optimized possesses a fast and steady gait and flexible steering ability.