LSPP: A Novel Path Planning Algorithm Based on Perceiving Line Segment Feature

Abstract

Path planning in the automatic driving system generally uses point features to evaluate the influence of obstacles on the robot, leading to missing information or increasing computational burden. This paper proposes a novel path planning with line segment (LSPP) algorithm from the viewpoint of line segment, which is easy to be obtained directly from sensors. In LSPP, an artificial potential field (APF) based on the azimuth and distance (ADAPF) from obstacles to the robot is first constructed for implementation of integral calculation. Unlike the traditional APF which only considers the nearest distance from obstacles to the robot, ADAPF can effectively assess the influence of boundary points on the robot by fully utilizing the information of obstacles. Based on ADAPF, boundary points are integrated into lines as edges of the drivable area in a specialized coordinate system. Then, a multi-loss function is designed to guarantee that the robot is far away from obstacles to the optimized path, and thus solved by a modified Dogleg (MDL) algorithm combined with symmetric rank-1 (SR1) method based on adaptive initial trust-region radii. Finally, a smooth and safe path is provided in LSPP with the requirements of speed and quality being satisfied simultaneously. Real-world experimental results validate the robustness and the effectiveness of LSPP.