Distributed multi-target search and tracking using the PHD filter
Distributed multi-target search and tracking using the PHD filter
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DOI：
10.1007/s10514-019-09840-9
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发表时间：
2020-03-01
影响因子：
3.5
通讯作者：
Dames, Philip M.
中科院分区：
文献类型：
--
作者：
Dames, Philip M.
本文提出了一种分布式估计和控制算法，使一队移动的机器人搜索和跟踪未知数量的目标。这些目标可以是静止的或移动的，并且随着目标进入和离开感兴趣区域，目标的数量可以随时间变化。机器人配备了具有有限视野的传感器，并且可能会经历假阴性和假阳性检测。机器人使用一种新的，分布式的概率假设密度（PHD）过滤器，占传感器的限制，估计目标的数量和目标的位置。然后，机器人使用劳埃德算法，这是一种分布式控制算法，已被证明对覆盖和搜索任务有效，以驱动它们在环境中的运动。我们利用PHD滤波器的输出作为Lloyd算法中的重要性加权函数。这导致机器人被吸引到可能包含目标的区域。我们证明了我们所提出的算法的有效性，包括一个基于覆盖的控制器与统一的重要性加权函数的比较，通过一系列广泛的模拟实验。这些实验表明，10-100个机器人的团队成功地在2D和3D环境中跟踪10-50个目标。
This paper proposes a distributed estimation and control algorithm that enables a team of mobile robots to search for and track an unknown number of targets. These targets may be stationary or moving, and the number of targets may vary over time as targets enter and leave the area of interest. The robots are equipped with sensors that have a finite field of view and may experience false negative and false positive detections. The robots use a novel, distributed formulation of the Probability Hypothesis Density (PHD) filter, which accounts for the limitations of the sensors, to estimate the number of targets and the positions of the targets. The robots then use Lloyd's algorithm, a distributed control algorithm that has been shown to be effective for coverage and search tasks, to drive their motion within the environment. We utilize the output of the PHD filter as the importance weighting function within Lloyd's algorithm. This causes the robots to be drawn towards areas that are likely to contain targets. We demonstrate the efficacy of our proposed algorithm, including comparisons to a coverage-based controller with a uniform importance weighting function, through an extensive series of simulated experiments. These experiments show teams of 10-100 robots successfully tracking 10-50 targets in both 2D and 3D environments.