极端环境中的航空测量机器人:用蜂群无人机绘制火山二氧化碳排放图

John Ericksen, G. M. Fricke, S. Nowicki, T. Fischer, Julie Hayes, Karissa Rosenberger, Samantha R. Wolf, R. Fierro, M. Moses
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引用次数: 3

摘要

我们提出了使用航空机器人自主协作调查火山二氧化碳排放的方法。二氧化碳是火山爆发的有用预测因子,也是一种有影响的温室气体。然而,目前的二氧化碳测绘方法既危险又低效,因此,只有一小部分二氧化碳排放火山被调查过。我们开发了测量火山二氧化碳排放的算法和平台。Dragonfly无人驾驶飞行器(UAV)平台能够在恶劣环境中进行长时间的二氧化碳收集飞行。我们在蜻蜓机器人团队上实现了两种调查算法,并证明它们可以有效地绘制气体排放图并定位最高气体浓度。我们的实验最终在卡尔德拉山谷超级火山边缘一个充满挑战的现实世界环境中成功地进行了协作光栅化和梯度下降算法的现场测试。这两种算法都将多个植绒无人机视为一种分布式柔性仪器。多架无人机的同时传感使科学家对气体浓度和排放源位置的估计更有信心。这些方法也适用于一系列其他空气浓度测绘任务,如管道泄漏检测和污染物定位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Aerial Survey Robotics in Extreme Environments: Mapping Volcanic CO2 Emissions With Flocking UAVs
We present methods for autonomous collaborative surveying of volcanic CO2 emissions using aerial robots. CO2 is a useful predictor of volcanic eruptions and an influential greenhouse gas. However, current CO2 mapping methods are hazardous and inefficient, as a result, only a small fraction of CO2 emitting volcanoes have been surveyed. We develop algorithms and a platform to measure volcanic CO2 emissions. The Dragonfly Unpiloted Aerial Vehicle (UAV) platform is capable of long-duration CO2 collection flights in harsh environments. We implement two survey algorithms on teams of Dragonfly robots and demonstrate that they effectively map gas emissions and locate the highest gas concentrations. Our experiments culminate in a successful field test of collaborative rasterization and gradient descent algorithms in a challenging real-world environment at the edge of the Valles Caldera supervolcano. Both algorithms treat multiple flocking UAVs as a distributed flexible instrument. Simultaneous sensing in multiple UAVs gives scientists greater confidence in estimates of gas concentrations and the locations of sources of those emissions. These methods are also applicable to a range of other airborne concentration mapping tasks, such as pipeline leak detection and contaminant localization.
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