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

3D Move to See: Multiperspective visual servoing towards the next best view within unstructured and occluded environments In this paper we present a novel approach termed 3D Move to See (3DMTS) which is based on the principle of finding the next best view using a 3D camera array and a robotic manipulator to obtain multiple samples of the scene from different perspectives. Distinct from traditional visual servoing and next best view approaches, the proposed method uses simultaneouslycaptured multiple views, scene segmentation and an objective function applied to each perspective to estimate a gradient representing the direction of the next best view in a xe2x80x9csingle shotxe2x80x9d. The method is demonstrated within simulation and on a real robot containing a custom 3D camera array for the challenging scenario of robotic harvesting in a highly occluded and unstructured environment. We show, on a real robotic platform, that by moving the eyeinhand camera using the gradient of an objective function leads to a locally optimal view of the object of interest, even amongst occlusions. The overall performance of the 3DMTS approach obtains a mean increase in target size of 29.3% compared to a baseline method using a single RGBD camera, which obtained 9.17%. The results demonstrate qualitatively and quantitatively that the 3DMTS method performed better in most scenarios, and yielded three times the target size compared to the baseline method. Increasing the target size in the image given occlusions can improve robotic systems detecting key object features for further manipulation tasks, such as grasping and harvesting.