Global path planning for robust Visual Servoing in complex environments

We incorporate sampling-based global path planning with Visual Servoing (VS) for a robotic arm equipped with an in-hand camera. The path planning accounts for a number of constraints: 1) maintaining continuous visibility of the target within the camera´s field of view, 2) avoiding visual occlusion of target features caused by the workspace obstacles, robot´s body, or the target itself, 3) avoiding collision with physical obstacles or self collision, and 4) joint limits. Incorporating these constraints enhances the applicability of VS to significantly more complex environments/tasks, thereby making the resulting VS much more robust. The proposed planner explores the camera space, i.e. 3D Cartesian space, for permissible camera paths satisfying the aforementioned constraints by iteratively extending a search tree in camera space and simultaneously tracking these paths in the robot´s joint space using a local planner. The planned camera path is then projected into the image space and tracked using an image-based visual servoing scheme. The validity and effectiveness of the proposed approach in accomplishing VS tasks in complex environments are demonstrated through a number of simulations on a 6-dof robot arm moving among obstacles.