基于区域的层次和基于路径的启发式方法用于多机器人覆盖路径规划与监控系统中的性能分析
Junghwan Gong1, Seunghwan Lee1
1School of Electronic Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
这项研究引入了一个新的多机器人覆盖路径规划 (MCPP) 监控框架,使用先进的算法,如遗传算法 (GA) 和殖民地优化 (ACO). 基于区域的MCPP在路径规划效率方面表现出色,而基于路径的MCPP提供了更好的覆盖平衡.
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:
- 多机器人覆盖路径规划 (MCPP) 对于监控至关重要,但往往受到天真方法的限制,特别是在考虑机器人传感范围 (SR) 时.
- 现有的MCPP方法主要集中在清洁任务上,缺乏包含SR的监控应用程序的综合策略.
研究的目的:
- 系统地探索适合监控应用的MCPP等级设计,并纳入传感范围 (SR) 因素.
- 提出一个一般的MCPP框架,全面解决基于路径和基于区域的结构,利用先进的算法进行增强的规划.
主要方法:
- 开发了六种不同的MCPP方法:两个以区域为基础的 (以GA或ACO为区域内的旅行销售员问题 (TSP) 的等级最大流量路由) 和四个以路径为基础的 (使用GA和ACO组合的全球-本地结构).
- 集成了先进的TSP解决方案,如遗传算法 (GA) 和殖民地优化 (ACO),而不是天真的方法.
- 在基于路径的方法中引入基于ACO或GA的地方规划,以改善路径质量并防止全球规划结果的退化.
主要成果:
- 与基于路径的方法相比,基于区域的MCPP方法在执行时间和总路径长度方面表现优越.
- 基于路径的MCPP方法具有较短置时间的优势.
- 拟议的基于区域的MCPP方法在路径规划效率方面表现出色,而基于路径的MCPP方法显示出更好的覆盖平衡.
结论:
- 基于区域和基于路径的MCPP结构之间的选择取决于特定的应用要求,平衡效率,路径长度和覆盖范围.
- 利用这两种方法的优势,可以在监控任务中高效地执行MCPP.
- 未来的工作重点是调整这些MCPP结构以适应各种现实条件,以确定最适合特定应用的方法.
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