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Towards 3-D distributed odor source localization: An extended graph-based formation control algorithm for plume tracking

机译:迈向3-D分布式气味源定位:一种基于图的扩展的羽流跟踪形成控制算法

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The large number of potential applications for robotic odor source localization has motivated the development of a variety of plume tracking algorithms, the majority of which work in restricted two-dimensional scenarios. In this paper, we introduce a distributed algorithm for 3-D plume tracking using a system of ground and aerial robots in formation. We propose an algorithm that takes advantage of spatially distributed measurements to track the plume in 3-D and lead the robots to the source by integrating three behaviors - upwind movement, plume centering, and Laplacian feedback formation control. We evaluate this strategy in simulation and with real robots in a wind tunnel. For a source close to the ground, results show that a team of robots running our algorithm reaches the source with low lateral error while also tracing the horizontal and vertical plume shape.
机译:机器人气味源定位的大量潜在应用推动了各种羽流跟踪算法的发展,其中大多数在受限的二维场景下工作。在本文中,我们介绍了一种利用编队中的地面和空中机器人系统进行3-D羽流跟踪的分布式算法。我们提出了一种算法,该算法利用空间分布的测量结果来跟踪3-D中的羽流,并通过整合三个行为(迎风运动,羽流居中和拉普拉斯反馈形成控制)将机器人引导至源头。我们在仿真中以及在风洞中使用实际机器人来评估此策略。对于离地面较近的源,结果表明,运行我们算法的一组机器人以较低的侧向误差到达了源,同时还跟踪了水平和垂直羽状形状。

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