A real-time randomized navigation method for biped robot

Abstract

Navigating autonomously in obstacle-cluttered environment is an important problem for biped robots. Path planning using classical methods, such as A* search and Dijkstra's algorithm, has made a good progress. However, these methods are inefficient. In this paper, a new algorithm named Heuristic Bi-directional Discrete Rapidly-explore Random Trees (HBD-RRTs) is presented. According to discrete stance model, two randomized-sampling-based planning trees from start state and target state are constructed respectively, where the path is generated as a set of stances by bi-directional search. Then a series of off-line gaits and an online gait generator are used to transform the path to motion trajectory. The simulation result in the platform of ZJU-Dance humanoid robot demonstrated that HBD-RRTs for biped robots is less-time-consuming and makes real-time planning possible.