轨迹:一种多机器人的火山羽流测量容错算法

J. Erickson, Abhinav Aggarwal, G. M. Fricke, M. Moses
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引用次数: 3

摘要

用无人机群测量火山二氧化碳通量带来了特殊的挑战。无人机必须能够跟随气体浓度梯度,同时容忍频繁的无人机损失。我们提出了一种求解该问题的算法,并证明了它的鲁棒性。LoCUS依靠群体协调和自我修复来解决任务。作为对比,我们还实现了MoBS算法,该算法源自先前发表的工作,允许无人机独立解决任务。通过无人机模拟比较了这些算法的有效性,发现LoCUS为火山测量问题提供了可靠、高效的解决方案。此外,支撑轨迹的新数据结构和算法在其他容错算法研究领域也有应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LoCUS: A Multi-Robot Loss-Tolerant Algorithm for Surveying Volcanic Plumes
Measurement of volcanic CO2 flux by a drone swarm poses special challenges. Drones must be able to follow gas concentration gradients while tolerating frequent drone loss. We present the LoCUS algorithm as a solution to this problem and prove its robustness. LoCUS relies on swarm coordination and self-healing to solve the task. As a point of contrast we also implement the MoBS algorithm, derived from previously published work, which allows drones to solve the task independently. We compare the effectiveness of these algorithms using drone simulations, and find that LoCUS provides a reliable and efficient solution to the volcano survey problem. Further, the novel data-structures and algorithms underpinning LoCUS have application in other areas of fault-tolerant algorithm research.
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