Maintaining Visibility of a Landmark using Optimal Sampling-based Path Planning

摘要

An approach to extend sampling-based path planning algorithms to include visual restrictions is presented. This approach imposes of visual constraints during the sampling and optimization processes. Four visual constraints are imposed during sampling: 1) keep the landmark within the sensor field of view, 2) avoid landmark occlusions, 3) maintaining landmark features near the image center, and 4) limit changes in landmark view orientation. These last two are imposed during path optimization. The robot task is to maintain these constraints, in an environment with obstacles, while the robot changes configurations. The sampling-based motion planning algorithm imposes and maintains both physical and visual restrictions. The process uses a collision checker to detect self-and obstacle-collisions, or landmark occlusions. To infer the landmark visibility, the algorithm dynamically builds a 3D model of camera field visibility as seen from the moving robot. To maintaining the landmark features close to the image center, a distance parameter from the field of view boundary to the landmark is used and optimized. The camera roll angle was included as another element to be optimized, limiting changes in orientation. The algorithm has been implemented, and both results in simulation and experiments using a real robot manipulator are presented.