在异质无人机网络中为动态任务分配提供层次框架和边际回报优化
Anxin Guo1, Zhenxing Zhang1, Ao Wu2
1Air Traffic Control and Navigation School, Air Force Engineering University, Xi'an 710000, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究引入了一个新的层次框架,用于协调多个无人机 (UAV),以改善任务分配. 基于边际回报的启发式算法 (MRBHA) 在复杂的动态环境中显著提高了任务价值.
科学领域:
- 机器人技术和自主系统
- 多代理系统 多代理系统
- 运营研究 运营研究
背景情况:
- 协调多样化的无人机 (UAV) 进行复杂的,多阶段的任务是具有挑战性的.
- 传统的线性模型与新兴的协同效应和动态的多代理合作作斗争.
- 现有的方法缺乏可靠的方法,在复杂的场景中有效地动态分配任务.
研究的目的:
- 为协调异质无人机提出一个新的层次框架.
- 引入一个理论结构来建模多代理合作,包括任务链 (MCs),执行路径 (EPs),任务网络 (TNs) 和解决方案空间 (SSs).
- 为复杂的任务开发一个高效的动态任务分配算法.
主要方法:
- 定义了一个基于任务链 (MC) 概念的层次框架.
- 建模的关键元素:任务链 (MCs),执行路径 (EPs),任务网络 (TNs) 和解决方案空间 (SSs).
- 制定了作为传感器-效应器-目标分配挑战的问题,并提出了基于边际回报的启发式算法 (MRBHA).
主要成果:
- 该MRBHA显著超过了标准的贪和随机分配策略.
- 与贪的任务相比,实现了14%的总预期任务价值.
- 与随机分配相比,实现了77%更高的预期总任务价值,证明了有效利用协同机会.
结论:
- 拟议的层次框架和MRBHA为复杂的无人机协调提供了强大而可扩展的解决方案.
- 该方法有效地管理了复杂环境中的动态任务分配.
- 潜在的应用包括搜救,环境监测和智能物流.
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