A central pattern generator approach to footstep transition for biped navigation

Zeyang Xia,Shaokui Weng,Hao Deng,Yangzhou Gan,Jing Xiong,Xue Zhang

doi:10.1177/1729881416682708

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.

Highlights

With better mobility and an anthropoid shape, biped robots are expected to assist human activities in daily environment, such as offices, hospitals, or at homes
The central pattern generator (CPG)-joint control method was presented to replace ISP to offer a new method of gesture transition trajectory in footstep planning for biped robot navigation and realize a self-adaption walking for irregular terrains
The advantage of the proposed method is that a whole walking motion was decomposed into five periodic motion types, and two of them, motion-I and II, are basic motion types for walking motion

Summary

Introduction

With better mobility and an anthropoid shape, biped robots are expected to assist human activities in daily environment, such as offices, hospitals, or at homes. To execute senior tasks in human-living environments, an effective footstep planning method for biped navigation is increasingly demanded. Previous navigation methods for biped robots can be roughly classified into two aspects. The first method is a deterministic sampling-based footstep planning, which is effective in open environments.[1,2,3] The second method is a random sampling-based method to goal-bias the footstep planning, which can accomplish navigation in some special environments, such as fields with local minimal or narrow passages.[4,5,6] Since human-living environment is complex and varied, in many research, the second method has been used for humanoid robot global navigation

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Conclusion