Employing Natural Terrain Semantics in Motion Planning for a Multi-Legged Robot

This paper considers motion planning for a six-legged walking robot in rough terrain, considering both the geometry of the terrain and its semantic labeling. The semantic labels allow the robot to distinguish between different types of surfaces it can walk on, and identify areas that cannot be negotiated due to their physical nature. The proposed environment map provides to the planner information about the shape of the terrain, and the terrain class labels. Such labels as “wall” and “plant” denote areas that have to be avoided, whereas other labels, “grass”, “sand”, “concrete”, etc. represent negotiable areas of different properties. We test popular classification algorithms: Support Vector Machine and Random Trees in the task of producing proper terrain labeling from RGB-D data acquired by the robot. The motion planner uses the A ∗ algorithm to guide the RRT-Connect method, which yields detailed motion plans for the multi-d.o.f. legged robot. As the A ∗ planner takes into account the terrain semantic labels, the robot avoids areas which are potentially risky and chooses paths crossing mostly the preferred terrain types. We report experimental results that show the ability of the new approach to avoid areas that are considered risky for legged locomotion.