A random sampling-based approach to goal-directed footstep planning for humanoid robots

Global path planning and navigation strategies for humanoid robots are different from those for mobile robots, since humanoid robots can step over or upon some obstacles. Sampling-based footstep planning is a goal-directed navigation approach for humanoid robots which considers both this unique locomotion capability and the global environment information. However, existing humanoid footstep planning methods are based on deterministic sampling strategies, which make them not practical in some special environments, such as areas with local minima or narrow passages. In order to improve the completeness of sampling-based footstep planning in the above environments, we propose a random sampling-based footstep planning approach based on rapidly-exploring random trees, which termed as Multi-RRT footstep planner. We also apply a goal-biased non-uniform strategy in order to improve the convergence rate of the Multi-RRT footstep planner. The proposed random sampling approach is verified by comparison with deterministic approach in numerical planning experiments.