Constrained Manipulator Visual Servoing (CMVS): Rapid robot programming in cluttered workspaces

This paper presents a model-free optimization framework for the visual servoing of eye-in-hand manipulators in cluttered environments. Visual feedback is used to solve for a set of feasible trajectories that bring the robot end-effector to a target object at a previously untaught location under a number of challenging constraints (i.e., whole-arm collisions, object occlusions, robot´s joint limits, camera´s sensing limits). A novel controller is proposed, which exploits the natural by-products of the teach-by-showing process, to help the robot navigate this non-convex space. Examining the user-demonstrated trajectories that lead up to the reference image, we use a combination of stochastic optimization techniques and classical optimization techniques to extract the relevant cost functions and constraints for servoing. We hypothesize that we can leverage the user´s sensory capabilities and knowledge of the workspace to alleviate the burden of modeling system constraints explicitly. We verify this hypothesis via realistic experiments on a Barrett WAM 7-DOF manipulator equipped with a Sony XC-HR70 camera to show the comparative efficacy of this approach.