动态环境下基于改进蚁群算法的机器人路径规划研究

Research of Improved Ant Colony Based Robot Path Planning Under Dynamic Environment

针对动态复杂条件下的移动机器人路径规划问题,根据全局静态环境先验知识,提出一种改进蚁群算法。在经典蚁群算法的基础上通过调整转移概率,限定信息素强度的上下界,并引入相关策略解决死锁问题,可以避免初期规划的盲目性,增加解的多样性,提高算法的全局搜索能力,进一步减小算法早熟的可能性。在规划过程中,根据动态障碍物运行方向的变化与否,提出了相应的碰撞避免策略,并针对环境突发状况引入Follow_wall行为进行改进。仿真实验证明,该算法优于经典蚁群算法,可有效地指导移动机器人避免环境中的动态障碍物,获取无碰最优或次优路径,并能更好地适应环境的变化。

This paper presents an improved ant colony algorithm for mobile robot path planning under dynamic complex conditions based on prior knowledge of global static environment. On the basis of conventional ant colony algorithm, by adjusting the transition probability, limiting the bounds of pheromone, and introducing relevant strategy to solve the deadlock problem, the improved ant colony algorithm not only can avoid the blindness of early planning and increase the diversity of solutions, but also can improve global search capability of the algorithm, and further reduce the possibility of algorithm prematurity as well. During the planning process, according to the direction changes of the dynamic obstacles, corresponding collision avoidance strategies are put forward. The Follw_wall behavior is introduced for unexpected situations in the environment. Simulation results show that the proposed algorithm is superior to conventional ant colony algorithm. It can effectively guide the mobile robot to avoid dynamic obstacles. Thus obtains a collision free optimal or suboptimal path, which adapts to the changes of the environment more effectively.