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.

A Novel Imagebased Path Planning Algorithm for EyeinHand Visual Servoing of a Redundant Manipulator in a Human Centered Environment This paper presents a novel imagebased pathplanning and execution framework for visionbased control of a robot in a human centered environment. The proposed method involves applying Rapidlyexploring Random Tree (RRT) exploration to perform ImageBased Visual Servoing (IBVS) while satisfying multiple task constraints by exploiting robot redundancy. The methodology incorporates dataset of robotxe2x80x99s workspace images for pathplanning and design a controller based on visual servoing framework. This method is generic enough to include constraints like FieldofView (FoV) limits, joint limits, obstacles, various singularities, occlusions etc. in the planning stage itself using task function approach and thereby avoiding them during the execution. The use of pathplanning eliminates many of the inherent limitations of IBVS with eyeinhand configuration and makes the use of visual servoing practical for dynamic and complex environments. Several experiments have been performed on a UR5 robotic manipulator to demonstrate that it is an effective and robust way to guide a robot in such environments.