Learning footstep planning on irregular surfaces with partial placements
Germán Castro, Claude Sammut · 2019
We present two contributions built upon on a previous footstep planner based on the ARA* search. Firstly, we have developed an improved foothold selection method using support polygons, to increase foothold availability in rough terrain. Secondly, we present a footstep classification method using the C5.0 algorithm, that takes advantage of cost similarity between adjacent steps. This is intended to learn feasibility and approximate transition costs for the ARA*planner.These contributions extend capabilities of the planner by increasing footstep availability and allowing to generate more complex plans, without compromising safety.