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.

Positionbased monocular visual servoing of an unknown target using online selfsupervised learning Visual servoing, i.e. control with visual information, is a valuable capability in many robotic applications. In particular, position based visual servoing (PBVS) estimates position information from the observed image to generate visual servo control. However, the estimation of the position of an unknown target using monocular images is still difficult due to the complexity of the image information. For the target estimation problem, we propose to integrate three complementary techniques for monocular visual servoing. First, to estimate the probability of a targetxe2x80x99s existence, the learning model with spatial features from convolution neural network is proposed. Second, the extended Kalman filter based on epipolar geometry estimates the 3D position of the target; moreover, from this 3D position, the perception model is trained online by selfgenerated virtual groundtruth. Finally, visual servo control is generated, and the resulting movement helps to construct epipolar geometry. Finally. the experimental validation is performed in a challenging setting involving occlusion and targetxe2x80x99s shape change.