A central pattern generator approach to footstep transition for biped navigation

Abstract

Existing sampling-based footstep planning method for biped navigation used an intermediate static posture for footstep transition. However, when adopting this approach, the robot is sensitive to modeling error and external environments, and also the transition between different gait patterns is unnatural. This article presents a central pattern generator approach to footstep transition for biped navigation. First, this approach decomposes the biped walking motion into five motion types and designs central pattern generator network for all joints of legs accordingly. Then, the central pattern generator parameters are simplified and the relationship between these parameters and footstep transition is formulated. By modifying the central pattern generator parameters, different walking gaits can be obtained. With sensing feedbacks, self-adaption walking on irregular terrains, such as walking on unknown sloped terrains and flat floor with tiny obstacles, is realized. Experiments were conducted both in simulator and on a physical biped robot. Results have shown that the proposed approach is able to generate gesture transition trajectory for biped robot navigation and realize a self-adaption walking for irregular terrains.