Robust sampling-based planning for uncalibrated visual servoing

Planning for vision-based robot control is a challenging open problem, especially in unstructured environments where models are not known a priori and sensor measurements contain errors and outliers. In this paper, we propose a statistically-robust randomized planning algorithm for a model-free eye-in-hand manipulator. The planner is built on the success and efficiency of the sampling-based planners, while incorporating robustness to outliers. In particular, we generalize the Rapidly-Exploring Random Tree (RRT) planner to the visual-motor space, the space that encodes both visual measurements and motor readings. The proposed planner is used in conjunction with a closed-loop visual control law. While the control law is entirely image based, the planner helps avoid joint limits, field-of-view constraints, and more importantly, visual occlusion of the target by unmodeled obstacles. The algorithm is validated in simulations as well as experiments with a WAM robot arm.