一种改进的机器人路径规划的蚁群算法

针对具有复杂回旋地形结构的机器人路径规划问题, 提出了一种改进的蚁群算法. 该算法引入自适应迁移概率函数实现蚁群具有正、反向运动的能力, 改善了算法的曲折迂回能力; 能见度信息中引入距离启发因素和障碍相交检测机制, 完成路径搜索与避障过程有机结合, 提高算法的搜索效率; 引入贪婪信息素更新策略和节点信息素分布, 降低了数据存储量, 改善了路径规划的效果和算法的收敛速度. 基于不同算法的比较仿真实验, 数值结果证实了该算法的有效性.

An improved ant colony optimization algorithm for robotic path planning

An improved ant colony optimization(ACO) algorithm for robotic path planning in a complex roundabout environment is proposed. The adaptive migratory probability function is introduced to make ants have the ability to travel in forward and backward direction of the target; thus, the ability in finding circuitous routes is improved. The distance elicitation factor and the crossing obstacle detection mechanism are introduced into the visibility information to integrate the path search with the obstacle-avoiding process for improving the search efficiency. The greedy pheromone updating strategy and the node pheromone distribution mode are studied to optimize the path planning result, convergence rate and data storage. The simulation results validate the effectiveness of the algorithm.