考虑到运动随机性和能量分配,对异质机器人的持续监控
1Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China; Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China.
ISA transactions
|August 21, 2025
概括
这项研究引入了使用异质机器人 (UGV和UAV) 进行环境检查的新型持续监测系统. 开发的算法提高了测量效率和隐私,同时优化了能源分配,使机器人导航更强大,更不可预测.
科学领域:
- 机器人和自主系统
- 环境监测
- 优化理论
背景情况:
- 持续监控需要机器人持续监控区域,同时管理运动和能量.
- 不同类型的机器人团队 (UGV和无人机) 为环境检查提供了独特的优势.
- 现有的方法缺乏保护隐私,节能持续监控和随机移动的可靠解决方案.
研究的目的:
- 开发一个保护隐私的持续监视机器人的框架.
- 解决动态,不可预测的机器人监控中的能源分配挑战.
- 提高实时环境检查应用中的测量效率.
主要方法:
- 使用基于马尔科夫链的随机运动进行概率测量的无人机框架.
- 用运动随机性和能量分配 (PSREA) 作为非凸优化问题的持续监控的制定.
- 开发基于集群的算法来简化任务网络和代的两阶段算法来解决优化问题.
主要成果:
- 拟议的算法在复杂的监控场景中有效处理大量的任务节点.
- 这些方法成功地解决了PSREA优化问题的非凸性.
- 与基准方法相比,数字结果显示检查性能有显著改善.
结论:
- 开发的框架和算法为保护隐私,节能持续监控提供了有效的解决方案.
- 这种方法提高了使用异质机器人的环境检查任务的测量效率和稳定性.
- 这项工作为优化复杂的机器人监控操作提供了一种新方法.
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