关键词: 车辆工程；移动机器人；路径规划；人工势场法；快速扩展随机树

Abstract: In recent years, the optimization algorithm proposed for the traditional artificial potential field (APF) method that was easy to fall into the local minimum problem still had the problems such as low applicability and low computational efficiency. Based on the shortcomings of some improved algorithms, a sampling-based rapidly exploring random tree (RRT) algorithm was innovatively introduced to pre-select a number of temporary target points on a static known map, which avoided the mobile robot from falling into the local minimum value area when the artificial potential field method was used. Meanwhile, the mobile robot carried out the obstacle avoidance strategy of real-time path planning in dynamic obstacle environment. Simulation test results show that the proposed method is simple and easy to implement; meanwhile, it combines the advantages of complete probability and good convergence of RRT algorithm with small calculation and high real-time performance of APF algorithm, which can adapt to the changes of dynamic environment and meet the requirements of dynamic obstacle avoidance of mobile robot.

Key words: vehicle engineering; mobile robot; path planning; artificial potential field algorithm; rapidly exploring random tree