基于改进人工势场-遗传算法的路径规划算法研究

Research on path planning algorithm based on improved artificial potential field-genetic algorithm

针对传统人工势场法解决移动机器人路径规划路问题时存在目标不可到达问题(GNRON)和局部最优的缺陷,提出一种建立在改进的人工势场模型之上结合遗传算法的并行搜索方法来寻求全局最优解的方法。通过引入填平势场使得势场函数能够跳出局部极小点,再将遗传算法和人工势场法两种方法相结合,利用人工势场法来优化采用遗传算法所得到的全局路径。仿真研究证明了所提出的改进算法的有效性,改进后算法能够在复杂的静态和动态环境中实现避障并找到最佳或接近最佳的移动机器人路径。

When the traditional artificial potential field method solves the path problem of mobile robot path planning,there are target unreachable problems(GNRON)and local optimal defects.This paper proposes a parallel search method to find a global optimal solution based on the improved artificial potential field model combined with the genetic algorithm method.By introducing the filling potential field,the potential field function can jump out of the local minimum point,and then use the artificial potential field method,combining the genetic algorithm and the artificial potential field method,to optimize the global path obtained by the genetic algorithm.Simulation studies demonstrate the effectiveness of the proposed improved algorithm.The improved algorithm can achieve obstacle avoidance and find the best or near-optimal mobile robot path in complex static and dynamic environments.