A Dynamic Local Path Planning Method for Outdoor Robot Based on Characteristics Extraction of Laser Rangefinder and Extended Support Vector Machine

作者：Yu, Lingli; Zhou, Kaijun^*

来源：International Journal of Pattern Recognition and Artificial Intelligence, 2016, 30(2): 1659004.

DOI：10.1142/S0218001416590047

• Outdoor robot
• local path planning
• laser rangefinder
• characteristic extraction and marking
• support vector machine (SVM)

For dynamic path planning problem under unstructured environment, firstly, successive edge following and least squares method (SEF-LSM) is adopted to extract environment characteristics of laser range finder data, and SEF-LSM with logical reasoning (SEF-LSM-LR) is proposed for dynamic obstacles characteristics detection. Furthermore, the perpendicularity (PERP) algorithm is utilized to identify dynamic vehicle, according to the perpendicularity attribute of vehicle. Secondly, all the laser range finder scanning points are marked as negative (-1) or positive (+1), and the scanning points of one dynamic obstacle are marked as the same label. Thirdly, extended support vector machine (ESVM) is designed for outdoor robot local path planning under unstructured environment, which consider the practical start-goal position and heading constraints, robot kinematic constraint, and curvature constraint, moreover, the emergency obstacle is regarded as disturbances during planning processing. Finally, the optimal path is chosen by the shortest distance evaluation function. Lots of outdoor simulations show that the proposed method solve the dynamic planning problem under unstructured environment, and their effectiveness performance are verified for outdoor robot path planning.

• 出版日期2016-2
• 单位中南大学; 湖南商学院