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基于改进的群优化算法与动态窗口方法相结合的交付机器人的路径规划

Limin Huang¹, Tao Hu¹, Jiabao Wei²

¹School of Mechanical Engineering, Chengdu University, Chengdu 610106, China.

Sensors (Basel, Switzerland)

|January 10, 2026

概括

一个新的混合算法通过将改进的群优化 (IACO) 与动态窗口方法 (DWA) 结合起来,改善了送餐机器人路径规划. 这有助于避免障碍物和改善路径的平滑性,优于传统方法.

科学领域:

机器人技术机器人技术机器人技术
人工智能的人工智能
优化算法优化算法

背景情况:

传统的群优化 (ACO) 在机器人路径规划中与缓慢的融合和动态的障碍回避作斗争.
动态窗口方法 (DWA) 在本地路径规划过程中可能会陷入局部最佳状态.

研究的目的:

开发一种混合路径规划算法,将改进的群优化 (IACO) 与动态窗口方法 (DWA) 集成在一起.
为了提高聚合速度,路径流性和动态障碍回避能力的餐饮机器人.

主要方法:

从引力搜索算法引入了引力概念到IACO以指导搜索.
实现了路径排序和增强的激素更新,以实现更快的融合和更顺的路径.
利用IACO全球路径中的关键节点作为DWA的多个本地目标点,以实现动态避障.

主要成果:

与静态环境中的ACO相比,IACO减少了路径长度高达30.03%,路径转高达71.43%.
拟议的融合算法成功地规划了光滑的路径,并在动态实验中避免了静态和动态障碍.
在静态比较实验中,IACO在其他测试的算法中表现出优越的性能.

结论:

混合IACO-DWA算法为餐饮配送机器人的路径规划效率和有效性提供了显著的改进.
这种方法解决了传统ACO和DWA的局限性,提供了强大的动态避障和更顺的轨迹.

关键词:

算法组合的算法组合.殖民地优化算法殖民地优化算法动态窗口方法动态窗口方法.配送食品的机器人提供食物.路径规划路径规划路径规划

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