改进蚁群融合DWA算法的移动机器人路径规划

为实现机器人在动态环境下的自主导航，基于蚁群算法规划出全局最优路径的情况下采用DWA算法进行局部避障。根据距离障碍栅格的远近计算邻接栅格的初始信息素，提出初始信息素不均匀分配原则；对启发式函数进行自适应调整的改进，提高算法的搜索速率；利用狼群法则改进信息素更新方式，对最优、最差和普通层蚂蚁进行分类更新，提高算法的寻优能力；使用二次路径优化的方法，有效减少路径长度，提高路径的平滑度；以蚁群算法全局规划路径的关键点为目标点，采用DWA算法进行局部路径规划。仿真结果表明：改进后的融合算法能减少最优路径长度，减少路径转弯次数且有效躲避障碍物。

Mobile Robot Path Planning Based on Improved Ant Colony and DWA Algorithm

In order to realize the autonomous navigation of mobile robot in dynamic environment, DWA algorithm is used to avoid local obstacles based on the global optimal path planned by ant colony algorithm. The initial pheromone of the adjacent grids is calculated according to the distance from the obstacle grid, and the principle of uneven distribution of the initial pheromone is proposed; the heuristic function is improved by adaptive adjustment to improve the search rate of the algorithm; the wolf group rule is used to improve the pheromone update mode, and the optimal, worst and ordinary layer ants are classified and updated to improve the optimization ability of the algorithm; The method of secondary path optimization is used to effectively reduce the length of the path and improve the smoothness of the path; Taking the key points of the global planning path of the ant colony algorithm as the target points, the DWA algorithm is used for local path planning. The simulation results show that the improved fusion algorithm can reduce the length of the optimal path, reduce the number of turns and effectively avoid obstacles.