改进多策略蚁群算法在机器人路径规划中的应用  

Application of Multi-Strategy Ant Colony Optimization Algorithm in Robot Path Planning

作　　者：郭娜苹 马小华 杨璐 高岳林 GUO Naping;MA Xiaohua;YANG Lu;GAO YueLin(School of Mathematics and Information Science,North Minzu University,Yinchuan 750021,China;Collaborative Innovation Center for Scientific Computing and Intelligent Information Processing of Ningxia,Yinchuan 750021,China)

机构地区：[1]北方民族大学数学与信息科学学院,宁夏银川750021 [2]宁夏科学计算与智能信息处理协同创新中心,宁夏银川750021

出　　处：《西北工程技术学报(中英文)》2025年第1期30-37,共8页Ningxia Engineering Technology

基　　金：宁夏自然科学基金重点项目(2024AAC03150);北方民族大学重大专项(ZDZX201901);宁夏一流学科建设项目(NXYLXK2017B09);南京证券支持基础学科项目(N-JZQJCXK202201);北方民族大学创新创业项目(YCX24084)。

摘　　要：针对传统蚁群算法在求解机器人路径规划过程中存在收敛速度慢、容易陷入局部最优等不足,提出了一种改进的多策略蚁群优化算法。首先,根据方向信息优化初始信息素分布,降低初始阶段的盲目性;其次,在启发式函数中加入权重系数以提升灵活性,并在信息素蒸发系数中融入正态分布函数,加快收敛速度和后期探索能力;再次,引入基于方向的信息素扩散策略,增强蚂蚁间信息交流的目的性。通过与传统蚁群算法对比,验证了各改进策略的有效性。结果表明,在相同环境下,所提算法较蚁群算法在求解最优路径时提高了20.2%。最后,将改进后的算法与其他两种算法在不同复杂度和规模的栅格环境下进行对比,验证了其综合性能。In response to the limitations of the traditional Ant Colony Optimization(ACO)algorithm in robot path planning,such as slow convergence and susceptibility to local optima,an improved multi-strategy ACO algorithm is proposed.First,the initial pheromone distribution is optimized based on directional information to reduce randomness in the initial stages.Next,weight coefficients are added to the heuristic function to enhance flexibility,and a normal distribution is incorporated into the pheromone evaporation coefficient to accelerate convergence and improve the exploration ability in the later stages.Additionally,a direction-based pheromone diffusion strategy is introduced to enhance purposeful information exchange among ants.The effectiveness of each improvement strategy is validated through comparison with the ACO algorithm.Experimental results show that,in the same environment,the proposed algorithm improves the optimal path by 20.2%compared to ACO.Finally,the improved algorithm is compared with two other algorithms in grid environments of varying complexities and sizes,demonstrating its superior overall performance.

关 键 词：机器人 路径规划 蚁群算法 自适应 信息素更新 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]