弹性多机器人覆盖路径再分配使用布斯特罗菲顿分解用于环境监测
Junghwan Gong1, Hyunbin Kim1, Seunghwan Lee1
1School of Electronic Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
本研究提出了一种强大的多机器人路径规划方法,即使机器人失败,也能确保连续覆盖. 它适应性地重新分配任务,以保持效率和平衡剩余机器人的工作负载.
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
- 计算机科学 计算机科学
背景情况:
- 多机器人系统需要强大的任务分配,以实现连续运行.
- 机器人故障可能会破坏覆盖任务,并过载剩余的机器人.
研究的目的:
- 开发一种有弹性的多机器人覆盖路径规划方法.
- 确保在机器人故障时进行适应性的任务重新分配和平衡的工作负载.
主要方法:
- 利用布斯特罗菲登细胞分解来进行区域划分.
- 实施了基于传播的战略,以实现动态的任务重新分配.
- 在不同的环境条件下模拟了方法.
主要成果:
- 在各种环境中表现出强性和适应性.
- 通过最小的传播周期实现了快速有效的任务重新分配.
- 减少了任务变异,并保持了平衡的覆盖范围.
结论:
- 拟议的方法可确保在多机器人覆盖范围内最小的干扰和有效的工作负载分配.
- 该方法适用于复杂,动态环境中的实时应用.
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