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移动机器人路径规划是机器人的重要研究领域之一，根据机器人接收的任务和周围环境信息，为其寻找一条从起点到终点且没有碰撞的最优路径。针对蚁群算法存在的收敛速度慢、路径繁冗及易陷入局部最优等问题，本文提出了一种改进的蚁群算法。首先，通过融合具有目标导向性的Euclidean距离与Chebyshev距离以提高蚂蚁前期的搜索效率，并在启发函数中引入正态分布来提高路径的搜索精度；其次，在信息素更新机制中加入奖惩策略，通过强化优秀路径引导能力以提高算法收敛速度；然后，结合自适应信息素挥发因子动态调节搜索行为，增强全局寻优能力，降低陷入局部极值的风险；最后，通过剪枝操作，减少机器人转弯次数，缩短路径距离。通过在二维和三维环境中与其他算法进行仿真对比实验，结果表明，改进算法不仅能够找到最短路径，而且在运行时间上也表现出更高的搜索效率。
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 convergence, 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 highquality 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 optima. Finally, a pruning strategy is employed to reduce the number of turns and shorten the overall path length. Comparative 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.
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基本信息:
DOI：10.12194/j.ntu.20250414001
中图分类号:TP242;TP18
引用信息:
[1]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(01):24-31.DOI:10.12194/j.ntu.20250414001.
基金信息:
陕西省自然科学基金项目（2024JC-YBMS-015）
2025-04-14
2025
2025-07-03
2025-07-02
2025
1
2025-07-24
2025-07-24
2025-07-24