一种混合路径规划算法,结合了A*和改进的群优化与动态窗口方法,以提高仓库环境中的能源效率
Mingyu Wu1,2,3, Eileen Lee Ming Su2, Che Fai Yeong2
1Jiaxing Key Laboratory of Industrial Internet Security, Jiaxing Vocational and Technical College, Jiaxing, Zhejiang, China.
PeerJ. Computer science
|February 3, 2025
概括
一种新的混合路径规划算法,IACO+A*+DWA,显著减少了仓库中的能源消耗和路径长度. 这种新的方法提高了动态仓库环境的效率.
科学领域:
- 机器人和自动化 机器人和自动化
- 人工智能的人工智能
- 运营研究 运营研究
背景情况:
- 仓库自动化依赖于机器人系统的高效路径规划.
- 现有的算法经常与动态环境和能源优化作斗争.
- 正在探索混合方法,以结合不同规划策略的优势.
研究的目的:
- 开发和评估一种新的混合路径规划算法,以提高仓库环境中的能源效率.
- 将A*的启发式搜索,群优化 (ACO) 的优化以及动态窗口方法 (DWA) 的动态适应性结合起来.
- 为了证明拟议的IACO+A*+DWA算法的优越性,而不是现有的混合方法.
主要方法:
- 开发了一种新的混合路径规划算法,集成A*,群优化 (ACO) 和动态窗口方法 (DWA).
- 该算法被称为IACO+A*+DWA,在复杂的仓库场景中进行了测试.
- 性能根据能源消耗,路径长度,转次数和目标时间进行评估,并使用ANOVA进行统计分析.
主要成果:
- IACO+A*+DWA算法实现了平均能源消耗量为89.8 J,比ACO+A*+DWA低13.3%.
- 与其他混合方法相比,它导致路径长度更短 (95.94 m),转更少 (43).
- 该算法在达到目标时比GA+A*+DWA快1.5%,显示出更好的时间效率.
结论:
- IACO+A*+DWA算法显著降低能源消耗,并优化仓库环境中的路径特性.
- 它在能源效率,路径长度和速度方面的卓越性能使其非常适合在动态设置中的实时应用.
- 未来的研究将探索进一步优化和整合机器学习以提高适应能力.
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