A variety of approaches exist that tackle the problem of humanoid locomotion. The spectrum ranges from dynamic walking controllers that allow fast walking to systems that plan longer footstep paths through complicated scenes. Simple walking controllers do not guarantee collision-free steps, whereas most existing footstep planners are not capable of providing results in real time. Thus, these methods cannot be used, not even in combination, to react to sudden changes in the environment. In this paper, we propose a new fast search method that combines A∗ with an adaptive 3D action set. When expanding a node, we systematically search for suitable footsteps by taking into account height information. As we show in various experiments, our approach outperforms standard A∗-based footstep planning in both run time and path cost and, combined with an efficient map segmentation, finds valid footstep plans in 3D environments in under 50 ms.
['Philipp Karkowski', 'Stefan Oswald', 'Maren Bennewitz']
Real-time footstep planning in 3D environments