Learning Contact-aware CPG-based Locomotion in a Soft Snake Robot

Abstract

In this paper, we present a model-free learningbased control scheme for the soft snake robot to improve its contact-aware locomotion performance in a cluttered environment. The control scheme includes two cooperative controllers: A bio-inspired controller (C1) that controls both the steering and velocity of the soft snake robot, and an event-triggered regulator (R2) that controls the steering of the snake in anticipation of obstacle contacts and during contact. The inputs from the two controllers are composed as the input to a Matsuoka CPG network to generate smooth and rhythmic actuation inputs to the soft snake. To enable stable and efficient learning with two controllers, we develop a game-theoretic process, fictitious play, to train C1 and R2 with a shared potential-field-based reward function for goal tracking tasks. The proposed approach is tested and evaluated in the simulator and shows significant improvement of locomotion performance in the obstacle-based environment comparing to two baseline controllers.

Bio-inspired Locomotion Control for UBot Self-reconfigurable Modular Robot

“…The CPG method has been combined with chaos theory to integrate various information from the sensors, so that the robot can automatically adjust its motion according to the changes in the environment. Since the bionic locomotion control has been widely used in the limbless robot such as snake-like robot [24] [25], and in the limbed robot such as bipedal [ 26 ], quadruped [27 ], insect-like robot [28 ], it must can be used in the motion control of the self-reconfigurable robot system which has multi-mode configurations and multi-mode locomotion modes.…”