基于改进人工势场的AGV路径规划算法

在老旧仓库中使用传统人工势场算法进行路径规划时, 原本出现频率极低的与远目标端障碍物相撞、目标点不可达、局部极小值等缺陷出现的频率极大提高. 为提升人工势场算法寻径的成功率, 本文提出了改进人工势场算法, 对上述3种缺陷进行了修正, 并使用Matlab模拟仿真验证了算法的有效性. 在改进人工势场算法中, 通过对引力与斥力的改进, 有效解决了与远目标端障碍物相撞及目标点不可达问题. 通过引入临时障碍物, 则有效解决了局部极小值问题. 在实验部分, 针对不同仿真环境, 我们以路径长度和程序运行时间作为评价指标, 对比了传统人工势场算法与改进人工势场算法的路径规划效果. 实验结果显示不论环境中是否存在缺陷, 改进人工势场算法总优于传统人工势场算法.

AGV Path Planning Algorithm Based on Improved Artificial Potential Field

When the traditional artificial potential field algorithm is used for path planning in an old warehouse, defects such as collision with obstacles far from the target, an unreachable target point, and local minimums, which originally appear infrequently, occur much more frequently. To improve the success rate of the artificial potential field algorithm in path finding in an old warehouse, this paper proposes an improved artificial potential field algorithm that corrects the above three defects and uses Matlab simulation to verify the effectiveness of the algorithm. In the improved artificial potential field algorithm, the problems of collision with obstacles far from the target and an unreachable target point are solved through the improvement of gravitation and repulsion. The local minimum problem is effectively solved by introducing temporary obstacles. In the experimental part, for different simulation environments, we use path length and program running time as evaluation indicators to compare the path planning effects of the traditional artificial potential field algorithm and the improved artificial potential field algorithm. Experimental results show that the improved algorithm always outperforms the traditional algorithm regardless of the presence or absence of defects in the environment.