针对传统蚁群算法在移动机器人路径规划中存在搜索盲目性、收敛速度慢及路径转折点多等问题,提出了一种基于改进蚁群算法的移动机器人路径规划算法.首先,利用跳点搜索(Jump Point Search,JPS)算法不均匀分配初始信息素,降低蚁群前期盲目搜索的概率;然后,引入切比雪夫距离加权因子和转弯代价改进启发函数,提高算法的收敛速度、全局路径寻优能力和搜索路径的平滑程度;最后,提出一种新的信息素更新策略,引入自适应奖惩因子,自适应调整迭代前、后期的信息素奖惩因子,保证了算法全局最优收敛.实验仿真结果表明,在不同地图环境下,与现有文献结果对比,该算法可以有效地缩短路径搜索的迭代次数和最优路径长度,并提高路径的平滑程度.
Abstract
In order to address the drawbacks of traditional ant colony algorithm in mobile robot path planning,such as blind search,slow convergence speed,multiple path turning points,it proposes a mobile robot path planning algorithm based on improved ant colony algorithm.Firstly,the Jump Point Search(JPS)algorithm is utilized to unevenly distribute initial phero-mones,reducing the likelihood of blind search during the early stages of the ant colony.Then,a Chebyshev distance weighting fac-tor and turning cost are introduced to improve the heuristic function,enhancing the algorithm's convergence speed,global path op-timization capability,and smoothness of the search path.Finally,a novel pheromone update strategy is proposed that introduces an adaptive reward-punishment factor to adaptively adjust the pheromone reward-punishment factor during pre-and post-iteration pha-ses,ensuring the algorithm's global optimal convergence.Experimental simulation results demonstrate that,in various map environ-ments and compared to existing literature results,the proposed algorithm effectively reduces the number of iterations and optimal path length required for path search while increasing path smoothness.
关键词
蚁群算法/路径规划/跳点搜索算法/移动机器人/信息素启发
Key words
ant colony algorithm/path planning/Jump Point Search(JPS)algorithm/mobile robot/pheromone heuristic
维普
万方数据