Bi-RRT path extraction and curve fitting smooth with visual based configuration space mapping

Abstract

Path planning is the one of the most basic research areas in robotics. It simply concern about acquiring a safe path with admissible cost. In this study, we adapt bidirectional rapidly random exploring tree (Bi-RRT) path extraction to visual based configuration space map hosting obstacles and smooth result path with curve fitting models. Firstly, a map of the configuration space is created and robot, target positions are detected with threshold based object detection. There are two positions where two distinct RRT are launched on this map. These positions are robot initial position and target position. Both RRT try to reach target with random branches in each iterations. When one of these RRT branch intersect with other RRT branch, the algorithm is stopped. The acquired trajectory is the path between initial position and target position. But acquired path is generally close to the obstacles and unnecessary branches or jagged parts can be formed. Therefore, to provide safety object dilation over obstacles are used. Finally, the path is smoothed with curve fitting models. We conduct several experiments to evaluate Bi-RRT performance.