南通大学学报（自然科学版）2026，Vol.25Issue(1)：24-31,8.DOI:10.12194/j.ntu.20250414001
基于改进蚁群算法的移动机器人路径规划
Mobile robots path planning based on improved ant colony algorithm
摘要
Abstract
Path planning for mobile robots is considered one of the fundamental research areas in robotics.It involves determining an optimal,collision-free path from a start point to a target based on the assigned task and environmental perception.To address the limitations of the standard ant colony optimization(ACO)algorithm,including slow conver-gence,redundant paths,and susceptibility to local optima,this study proposes an improved ACO algorithm.Firstly,goal-oriented Euclidean and Chebyshev distances are fused to enhance early-stage search efficiency,and a normal distribution(Gaussian distribution)is introduced into the heuristic function to improve path search precision.Secondly,a reward-penalty strategy is incorporated into the pheromone update mechanism to reinforce the influence of high-quality paths and accelerate convergence.Thirdly,an adaptive pheromone evaporation factor is applied to dynamically adjust the search behavior,thereby enhancing global exploration and reducing the risk of becoming trapped in local op-tima.Finally,a pruning strategy is employed to reduce the number of turns and shorten the overall path length.Com-parative simulation experiments with other algorithms in both two-dimensional and three-dimensional environments demonstrate that the proposed algorithm not only yields shorter paths but also exhibits higher search efficiency in terms of running time.关键词
蚁群算法/移动机器人/路径规划/信息素/路径平滑Key words
ant colony optimization/mobile robot/path planning/pheromone/smooth path分类
信息技术与安全科学引用本文复制引用
苑俊辉,王晓东,马盈仓..基于改进蚁群算法的移动机器人路径规划[J].南通大学学报（自然科学版）,2026,25(1):24-31,8.基金项目
陕西省自然科学基金项目(2024JC-YBMS-015) （2024JC-YBMS-015）
