Humanoid Robot Next Best View Planning Under Occlusions Using Body Movement Primitives

Abstract

This work presents an approach for humanoid Next Best View (NBV) planning that exploits full body motions to observe objects occluded by obstacles. The task is to explore a given region of interest in an initially unknown environment. The robot is equipped with a depth sensor, and it can perform both 2D and 3D mapping. As main contribution with respect to previous work, the proposed method does not rely on simple motions of the head and it was evaluated in real environments. The robot is guided by two behaviors: a target behavior that aims at observing the region of interest by exploiting body movements primitives, and an exploration behavior that aims at observing other unknown areas. Experiments show that the humanoid is able to peer around obstacles to reach a favourable point of view. Moreover, the proposed approach results in a more complete reconstruction of objects than a conventional algorithm that only changes the orientation of the head.