基于改进人工势场法的移动机器人路径规划

针对传统人工势场法在路径规划过程中容易陷入陷阱区域和局部极小点的问题，提出了一种改进人工势场法。首先，提出安全距离概念，避免了不必要的路径，从而解决了路径过长和算法运行时间过长问题；然后，为避免机器人陷入局部极小点和陷阱区域，在算法中引入预测距离，使得算法可以在机器人陷入局部极小点或陷阱区域之前做出反应；最后，通过合理设置虚拟目标点，引导机器人避开局部极小点和陷阱区域。实验结果表明，改进算法可以有效解决传统算法容易陷入局部极小点和陷阱区域的问题；同时，相较于传统人工势场法，所提算法规划出的路径长度减少了5.2%，计算速度提高了405.56%。

Path planning of mobile robot based on improved artificial potential field method

Aiming at the problem that the traditional artificial potential field method is easy to fall into trap area and local minimum in the path planning process, an improved artificial potential field method was proposed. Firstly, the concept of safe distance was proposed to avoid unnecessary paths, so as to solve the problems of long path length and long algorithm running time. Then, in order to avoid the robot being trapped in the local minimum and trap area, the predictive distance was introduced into the algorithm, so that the algorithm was able to react before the robot being trapped in the local minimum or trap area. Finally, the robot was guided to avoid the local minimum and trap area by setting the virtual target points reasonably. The experimental results show that, the improved algorithm can effectively solve the problem that the traditional algorithm is easy to fall into the local minimum and trap area. At the same time, compared with those of the traditional artificial potential field method, the path length planned by this proposed algorithm is reduced by 5.2% and its speed is increased by 405.56%.