人工势场法在移动机器人路径规划中的改进

随着智慧工厂的逐渐发展, 移动机器人在工厂中的应用越来越广泛, 但是在工厂中障碍物较多, 使用传统人工势场法容易产生目标不可达以及局部最小值等问题. 本文针对传统人工势场法在路径规划中出现的目标不可达以及局部最优解进行改进. 首先针对目标不可达的情况, 采用新斥力势场函数, 通过对原人工势场法中的斥力势场函数增加影响函数, 从而解决目标不可达; 其次针对局部最优解, 采用人工势场法与模拟退火法相结合的方法, 利用模拟退火法中的增设子目标点, 打破平衡状态, 从而走出障碍物. 最后通过Matlab对比, 本文算法在10个障碍物中比其他文献中算法的行驶时间提升6.70%, 路径长度减少9.20%. 本文算法在20个障碍物中比其他文献中算法的行驶时间提升9.10%, 路径长度减少12.10%.

Improvement of Artificial Potential Field Method in Path Planning of Mobile Robots

With the gradual development of smart factories, mobile robots are applied more and more widely in the factory. However, as there are many obstacles in the factory, the traditional artificial potential field method is easy to produce unreachable targets and local minimum values and other problems. This study improves the unreachable target and the local optimal solution of the traditional artificial potential field method in path planning. Firstly, a new repulsive potential field function is adopted to solve the problem of unreachable targets by adding an influence function to the repulsive potential field function in the original artificial potential field method. Secondly, for the local optimal solution, the artificial potential field method is combined with the simulated annealing method, and the additional subpoints in the simulated annealing method are applied to break the equilibrium state, so as to get out of the obstacles. Finally, through Matlab comparison, the travel time of the proposed algorithm in 10 obstacles is improved by 6.70% and the path length is reduced by 9.20% compared with algorithms in other literature. In 20 obstacles, the travel time of the proposed algorithm is improved by 9.10% and the path length is reduced by 12.10% compared with algorithms in other literature.