LoCUS: A Multi-Robot Loss-Tolerant Algorithm for Surveying Volcanic Plumes
LoCUS: A Multi-Robot Loss-Tolerant Algorithm for Surveying Volcanic Plumes
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DOI：
10.1109/irc.2020.00025
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
J. Erickson;Abhinav Aggarwal;G. M. Fricke;M. Moses
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文献类型：
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作者：
J. Erickson;Abhinav Aggarwal;G. M. Fricke;M. Moses
通过无人机群测量火山CO2通量带来了特殊的挑战。无人机必须能够遵循气体浓度梯度，同时容忍频繁的无人机丢失。我们提出的LoCUS算法作为解决这个问题，并证明其鲁棒性。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，该算法来自以前发表的工作，允许无人机独立解决任务。我们使用无人机模拟比较这些算法的有效性，并发现LoCUS提供了一个可靠和有效的解决方案，火山调查问题。此外，新的数据结构和算法的基础LoCUS在其他领域的容错算法研究的应用。
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.