基于自适应变步长蚁群算法的路径规划研究

Research on path planning based on adaptive variable step size ant colony algorithm

针对移动机器人路径规划避障问题,提出了一种基于自适应变步长的改进蚁群算法。该算法采用栅格法建模,在静态规划部分通过调整信息素更新规则以解决传统蚁群算法收敛速度慢、易陷入局部最优等缺陷,然后通过基于中心点思想的平滑策略进行平滑操作。在动态障碍物环境下,根据蚁群算法全局静态环境先验知识,通过障碍物栅格个数占视野范围内所有栅格个数的比例即占空比策略选择下一步移动栅格,从而在成功避障的前提下降低路径长度,增强路径平滑度。仿真实验表明,自适应变步长蚁群算法在动态障碍物环境下能够很好的完成避障任务,具有很强的可行性和时效性。

Aiming at the obstacle avoidance problem of mobile robot path planning,an improved ant colony algorithm based on adaptive variable step size is proposed.The algorithm is modeled by the grid method.In the static planning part,the pheromone update rule is adjusted to solve the shortcomings of the traditional ant colony algorithm,such as slow convergence speed and easy to fall into local optimum.Then,the smoothing operation is performed by the smoothing strategy based on the central point idea.In the dynamic obstacle environment,according to the global static environment prior knowledge of the ant colony algorithm,the next step is to move the grid by the ratio of the number of obstacle grids to the total number of grids in the field of view,ie the duty cycle strategy.Reduce the path length and enhance the path smoothness under the premise of successfully avoiding obstacles.Simulation experiments show that the adaptive variable-step ant colony algorithm can complete the obstacle avoidance task in the dynamic obstacle environment,and it has strong feasibility and timeliness.