基于A*算法和人工势场法的移动机器人路径规划

针对复杂非结构化环境下移动机器人的路径规划问题，提出了将全局与局部规划算法相融合的路径规划方法。首先，对传统A*方法进行了有效的改进，新的A*算法能够完成机器人的路径规划任务，利用二次A*搜索方法得到了优化后的路径点，缩短了移动机器人的行驶路径。进一步，动态切点法可以有效地对已规划路径进行平滑处理；然后，综合考虑路径和环境的情况，采用改进的人工势场方法对移动机器人进行了局部路径规划，通过增设虚拟子目标的方法解决局部极小值问题，利用自适应步长调节算法对移动机器人的步长进行了动态优化；最后，针对不同场景，利用数值仿真将该算法与传统算法进行比较，结果表明该算法在不同环境路径规划的问题上具有一定的先进性和优越性。

Path Planning of Mobile Robots Based on A* Algorithm and Artificial Potential Field Algorithm

A hybrid algorithm was introduced based on the global and local path planning for the mobile robot navigations and collision avoidances under complex and unstructured environments. Firstly, this paper makes effective improvement on the traditional A* method. The new A* algorithm can complete the robot's path planning task. The optimized path point was obtained by using the quadratic A* search method, and the traveling path of mobile robot was shortened. Furthermore, dynamic tangential point method could effectively smooth the planned path. Secondly, considering the path and environment, the improved artificial potential field method was adopted to carry out the local path planning for the mobile robot. The problem of local minimum value was solved by adding virtual subtargets. The adaptive step size adjustment algorithm was used to dynamically optimize the step size of the mobile robot. Finally, according to different scenarios, the proposed algorithm was compared with the traditional algorithm by numerical simulation, and the results show that the proposed algorithm has some advantages in the path planning under different environments.