论文标题
基因座：用于测量火山羽流的多机器人损失算法
LoCUS: A multi-robot loss-tolerant algorithm for surveying volcanic plumes
论文作者
论文摘要
通过无人机群来测量火山二氧化碳通量会带来特殊的挑战。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损失。我们将基因座算法作为解决此问题的解决方案并证明其稳健性。基因座依靠群协调和自我修复来解决任务。作为对比的一点，我们还实施了从先前发表的工作中得出的MOBS算法，该算法使无人机可以独立解决任务。我们使用无人机模拟比较了这些算法的有效性，并发现基因座为火山调查问题提供了可靠，有效的解决方案。此外，基于基因座的基础的新型数据结构和算法在其他容忍算法研究的领域中进行了应用。
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.
