A MOTION CONTROL MODEL FOR MULTI-LEGGED ROBOTS BASED ON NEURAL NETWORKS

This research aims to optimise the gait planning and joint mechanical structure of multi-legged robots in a specific work environment. The motion equation of a multi-legged robot based on D-H parameters was used, and the Lagrangian was used to derive the dynamic equation to optimise energy allocation. Recurrent neural networks (NNs) were used for gait planning research in the complex terrain. These results confirm that the optimum hardness of the torsion spring is 714 N*mm/deg, and the output torque is the smallest. The error between simulation results and theoretical results is 2%. The simulation results show that this optimisation method can save the torsion spring hardness and reduce the energy consumption by 53%. These results indicated that the recurrent NN-based motion control models had achieved significant results in joint mechanical structure optimisation and gait planning. This study provides strong theoretical support for the motion control of multi-legged robots in specific environments, and is expected to promote gait planning and optimisation in practical applications.