Gait Adaptation After Leg Amputation of Hexapod Walking Robot Without Sensory Feedback

Abstract

In this paper, we address the adaptation of the locomotion controller to change of the multi-legged walking robot morphology, such as leg amputation. In nature, the animal compensates for the amputation using its neural locomotion controller that we aim to reproduce with the Central Pattern Generator (CPG). The CPG is a rhythm-generating recurrent neural network used in gait controllers for the rhythmical locomotion of walking robots. The locomotion corresponds to the robot’s morphology, and therefore, the locomotion rhythm must adapt if the robot’s morphology is changed. The leg amputation can be handled by sensory feedback to compensate for the load distribution imbalances. However, the sensory feedback can be disrupted due to unexpected external events causing the leg to be damaged, thus leading to unexpected motion states. Therefore, we propose dynamic rules for learning a new gait rhythm without the sensory feedback input. The method has been experimentally validated on a real hexapod walking robot to demonstrate its usability for gait adaptation after amputation of one or two legs.