A Maneuverable Winding Gait for Snake Robots Based on a Delay-Aware Swing and Grasp Framework Combining Rules and Learning Methods

Abstract

Due to the high redundant degree of freedom characteristics of snake robots, their joint lever arms tend to be very long and often result in torque saturation, especially in the case of inter-tree motion. Traditional static planning methods based on curve segments connecting or wave functions are often limited by torque saturation and cannot meet the requirements of inter-tree motion of snake robots. Therefore, in this letter, a delay-aware swing and grasp framework combining rules and learning methods (DSG) is proposed for extending the inter-tree motion capability of snake robots. Specifically, first, to overcome the torque saturation problem, a joint torque direction determination rule is proposed to fully utilize the kinetic energy of the snake robot and enable the robot to move in the desired manner. Then, the DSG reduces the exploration space of the policy and guarantees the performance under delay, and based on which a maneuverable winding gait is designed to enable it to wrap around the target horizontal branch with maneuverability. Simulation and sufficient experiment results demonstrate that the proposed reinforcement learning (RL) controller has low torque requirement, high robustness under high delay, fast motion speed, and high generalizability.