Supervised footstep planning for humanoid robots in rough terrain tasks using a black box walking controller

In recent years, the numbers of life-size humanoids as well as their mobility capabilities have steadily grown. Stable walking motion and control for humanoid robots are already well investigated research topics. This raises the question how navigation problems in complex and unstructured environments can be solved utilizing a given black box walking controller with proper perception and modeling of the environment provided. In this paper we present a complete system for supervised footstep planning including perception, world modeling, 3D planner and operator interface to enable a humanoid robot to perform sequences of steps to traverse uneven terrain. A proper height map and surface normal estimation are directly obtained from point cloud data. A search-based planning approach (ARA*) is extended to sequences of footsteps in full 3D space (6 DoF). The planner utilizes a black box walking controller without knowledge of its implementation details. Results are presented for an Atlas humanoid robot during participation of Team ViGIR in the 2013 DARPA Robotics Challenge Trials.