Biomimetic intelligent motion control method for quadruped robot with manipulator

Abstract

Installing a multi-degree-of-freedom manipulator on a quadruped robot is an important way to expand the application capability of quadruped robots. However, the introduction of the manipulator will greatly increase the difficulty of motion control of the quadruped robot, such as the poorer stability of the system and the difficulty of motion coordination between the quadruped and the manipulator. Aiming at the above problems, a biomimetic intelligent motion control method based on reinforcement learning for a quadruped robot with a manipulator is proposed, which draws on the idea that quadrupeds swing their tails to increase stability. The policy network and the state estimation network are trained separately. The robustness of the algorithm is improved by adding state-space noise and randomizing the robot dynamics parameters. Curriculum learning is introduced to obtain a more robust training direction. Simulation results demonstrate that the control strategy obtained through training effectively improves the robot’s motion stability and enhances the robot’s ability to resist external disturbances by actively moving the manipulator. During rapid motion, the strategy of dynamically adjusting the manipulator improves robot survivability by about 20% compared to the static strategy of the manipulator.