Hierarchical RRT for humanoid robot footstep planning with multiple constraints in complex environments

Abstract

Humanoid robots have abilities of stepping over or onto obstacles, which is different from wheeled robots. However it may be difficult to apply the ordinary motion planning methods such as Rapidly-exploring Random Trees (RRT) to humanoid robots directly. Because these kinds of methods only consider to circumvent obstacles and ignore the constraint of balance. Aiming at dealing with these problems in one frame, a novel approach based on hierarchical RRT is used to plan the footstep for humanoid robots. It is designed according to three basic constraints: a transition model based gait generator, an inverted pendulum based balance controller and a collision detection based path planner. First, a set of layered transition model is utilized to revise the footstep according to the terrain condition, which is able to take full use of the motion ability as well as improve the efficiency. Then, a hierarchical strategy is exploited to select the feasible foot location to be added in the random tree based on the results of collision checking and balance control. Finally, a dynamic RRT method is introduced in our work to revise paths in changing environments. Different experiments are given to verify the feasibility and performance of the proposed approach in complicated environments with both dynamic and static obstacles.