Distributed online assignment of charging stations in persistent coverage control tasks based on LP relaxation and ADMM
Distributed online assignment of charging stations in persistent coverage control tasks based on LP relaxation and ADMM
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基于LP松弛和ADMM的持续覆盖控制任务中充电站分布式在线分配
DOI：
10.1080/18824889.2022.2125246
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Hatanaka Takeshi
中科院分区：
文献类型：
--
作者：
Lu Zhiyuan;Yamashita Shunya;Yamauchi Junya;Hatanaka Takeshi
本文研究了在持续覆盖控制任务中无人机网络的充电站的分布式在线分配。为了确保不仅在运动中而且在能量上的持久性，无人机需要在电池耗尽之前回到充电站。文献中提出了基于所谓的控制障碍函数的能量持久性覆盖控制方案。然而，这些方法假设无人机与充电站之间的固定对应关系，但总是返回到预先分配的站不一定是有效的决策，即约束可能妨碍无人机的监视行为。因此，动态地将充电站重新分配给无人机有望提高覆盖性能。为此，我们制定了一个在线分配问题的充电站的控制障碍函数值确定的参数在真实的时间，并完全放松制定的优化问题的线性规划问题。然后，我们提出了一个分布式解决方案的问题的基础上ADMM和整体部分分布式控制架构，包括持久的覆盖控制和在线分配的充电站。最后通过蒙特卡洛仿真验证了该控制系统的有效性。
This paper investigates distributed online assignment of charging stations for a drone network in a persistent coverage control task. To ensure persistency not only in motion but also in energy, drones need to go back to charging stations before running out of their batteries. Coverage control schemes with energy persistency were presented in the literature based on so-called control barrier functions. These methodologies, however, assume a fixed correspondence between a drone and a charging station, but always returning to a preassigned station is not necessarily an efficient decision, namely the constraint may hinder the monitoring behaviour of the drones. Dynamically reassigning charging stations to drones is thus expected to enhance the coverage performance. To this end, we formulate an online assignment problem of charging stations with parameters determined by the control barrier function values in real time, and exactly relax the formulated optimization problem to a linear programming problem. We then propose a distributed solution to the problem based on ADMM and the overall partially distributed control architecture including persistent coverage control and online assignment of charging stations. The control system is finally demonstrated through Monte Carlo simulation.
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影响因子：
3.8
作者：
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通讯作者：
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DOI：
10.23919/siceiscs54350.2022.9754518
发表时间：
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期刊：
2022 SICE International Symposium on Control Systems (SICE ISCS)
影响因子：
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作者：
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通讯作者：
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DOI：
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发表时间：
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期刊：
2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)
影响因子：
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DOI：
--
发表时间：
2019
期刊：
2019 12th Asian Control Conference (ASCC)
影响因子：
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作者：
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