协调协作型异质机器人系统的启发式方法,以收获自动化与大小约束
Hyeseon Lee1, Jungyun Bae1,2, Abhishek Patil1
1Department of Mechanical and Aerospace Engineering, Michigan Technological University, Houghton, MI 49931, USA.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
开发了三种启发式方法,用于多机器人收获协调,优化任务分配和农业环境中的路由. 这些方法为复杂的机器人协调挑战提供了可行的解决方案.
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
- 运营研究 运营研究
背景情况:
- 在农业中协调异构的机器人系统,特别是收获,由于任务分配,路由和调度需求而复杂.
- 实时传感和快速协调更新对于高效的农业操作至关重要.
研究的目的:
- 开发和比较三种用于农业收获自动化多代理协调的启发式方法.
- 为了应对机器人尺寸限制的挑战,并优化任务完成时间.
主要方法:
- 灵感来源于组合优化的初级双重工作负载平衡.
- 贪的任务分配与代本地优化.
- 基于大型语言模型 (LLM) 的约束处理通过提示工程.
主要成果:
- 这三种启发式方法都为农业收获自动化提供了可行的解决方案.
- 这些方法在广泛的模拟中证明了合理的解决方案质量.
- 原始双重和贪的方法专注于工作负载平衡和路线优化,而LLM方法处理了约束.
结论:
- 开发的启发式方法显示了现实世界农业应用的巨大潜力.
- 这些方法可以适应多机器人协调问题的变化.
- 该研究为解决异质多机器人系统的复杂协调挑战提供了有价值的见解.
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