陈智雄, 杨家伟, 肖楠, 田新成. 基于无线携能传输和多级边缘卸载的空地协作巡检算法[J]. 电网技术, 2022, 46(10): 3961-3969. DOI: 10.13335/j.1000-3673.pst.2021.1644
引用本文: 陈智雄, 杨家伟, 肖楠, 田新成. 基于无线携能传输和多级边缘卸载的空地协作巡检算法[J]. 电网技术, 2022, 46(10): 3961-3969. DOI: 10.13335/j.1000-3673.pst.2021.1644
CHEN Zhixiong, YANG Jiawei, XIAO Nan, TIAN Xincheng. Air-ground Cooperative Inspection Algorithm Based on Wireless Power Transfer and Multi-level Edge Offloading[J]. Power System Technology, 2022, 46(10): 3961-3969. DOI: 10.13335/j.1000-3673.pst.2021.1644
Citation: CHEN Zhixiong, YANG Jiawei, XIAO Nan, TIAN Xincheng. Air-ground Cooperative Inspection Algorithm Based on Wireless Power Transfer and Multi-level Edge Offloading[J]. Power System Technology, 2022, 46(10): 3961-3969. DOI: 10.13335/j.1000-3673.pst.2021.1644

基于无线携能传输和多级边缘卸载的空地协作巡检算法

Air-ground Cooperative Inspection Algorithm Based on Wireless Power Transfer and Multi-level Edge Offloading

  • 摘要: 变电站采用智能机器人和无人机可实现高效、自动设备巡检。地面机器人在地上和室内近距离巡检方面具有优势;无人机更加灵活,巡检范围和效率更大,但是易受供能等限制。为了充分发挥空地联合巡检的优势,文章提出一种基于无线携能传输和多级边缘卸载的地面机器人和无人机协作巡检算法。首先针对典型变电站场景,给出各级设备在本地计算和卸载时的能耗、速率和时延计算方法,并建立无线携能传输和无人机中继条件下的多级任务卸载模型。接着兼顾时延和能耗要求,将最优化巡检问题描述为马尔科夫决策过程,提出一种基于Q-Learning的最佳任务卸载算法。仿真对比验证了论文算法的有效性与可靠性,通过灵活的卸载算法可实现系统综合性能最大化。

     

    Abstract: The usage of intelligent robots and drones could make equipment inspections more efficient and automatic. Ground robots have several advantages in close inspections on the ground and indoors. UAVs are flexible, with greater inspection range and efficiency, but there are restrictions such as energy supply. In order to give full play to superiority of air-ground joint inspection, this paper proposed a ground-based robot and UAV cooperative inspection algorithm, which based on wireless power transfer and multi-level edge unloading. Firstly, in connection with the typical substation scenario, calculation methods of energy consumption, rate and time delay of all levels of equipment in local calculation and unloading were proposed, and a multi-level task unloading model under the conditions of wireless power transfer and UAV relay was established. Then, this study taking the requirements of time delay and energy consumption into account, the optimization inspection problem was described as a Markov decision process, and an optimal task offloading algorithm based on Q-Learning was proposed. The simulation comparison verified the effectiveness and reliability of this algorithm, and the flexible unloading algorithm could maximize the overall performance of the system.

     

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