改进人工势场法的移动机器人路径规划

传统人工势场法在路径规划过程中易陷入势场局部最小点和陷阱区域，面对较为复杂的障碍物环绕环境也难以规划出完整路径。针对这个问题，提出了一种改进人工势场法。引入机器人前进的方向向量，对斥力的生成和计算机制进行了调整以解决其处于局部最小点情况下无法继续规划路径的问题；添加了判断机制以识别周边环境状况，当机器人处于陷阱区域等复杂环境下时设立虚拟目标点以引导其向外运动从而摆脱陷阱区域。结果表明，改进算法可以有效解决传统算法容易出现的路径规划中断情况；同时与传统算法相比，其在随机障碍物环境中的规划路径长度减少，有效提高了路径规划效率。

Mobile Robots Path Planning Based on Improved Artificial Potential Field

The traditional artificial potential field is easy to fall into the local minimum point and trap area in the path planning process, and is difficult to plan a complete path when the robot surrounded by complex obstacles. An improved artificial potential field is proposed to solve these problems. Firstly, the direction vector of the robot forward is introduced to solve the problem that the robot cannot continue to plan the path when it is at the local minimum point, the generation and the computer system of the repulsive force are also adjusted for this problem. Secondly, a judgment mechanism is added to identify the surrounding environment. When the robot in a complex environment such as the trap area, a virtual target point is set to guide the robot to move outward to get rid of this place. The results show that the improved algorithm can effectively solve the path planning interruption that is easy to occur in traditional algorithms. At the same time, compared with the traditional algorithm, the planned path length in the random obstacle environment is reduced, which effectively improves the path planning efficiency.