Search-Based Footstep Planning

Efficient footstep planning for humanoid navigation through cluttered environments is still a challenging problem. Often, obstacles create local minima in the search space, forcing heuristic planners such as A* to expand large areas. Furthermore, planning longer footstep paths often takes a long time to compute. In this work, we introduce and discuss several solutions to these problems. For navigation, finding the optimal path initially is often not needed as it can be improved while walking. Thus, anytime search-based planning based on the anytime repairing A* or randomized A* search provides promising functionality. It allows to obtain efficient paths with provable suboptimality within short planning times. Opposed to completely randomized methods, anytime searchbased planners generate paths that are goal-directed and guaranteed to be no more than a certain factor longer than the optimal solution. By adding new stepping capabilities and accounting for the whole body of the robot in the collision check, we extend the footstep planning approach to 3D. This enables a humanoid to step over clutter and climb onto obstacles. We thoroughly evaluated the performance of search-based planning in cluttered environments and for longer paths. We furthermore provide solutions to efficiently plan long trajectories using an adaptive level-of-detail planning approach.