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.

中文翻译：

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使用PHD过滤器进行分布式多目标搜索和跟踪

本文提出了一种分布式估计和控制算法，该算法使一组移动机器人能够搜索和跟踪未知数量的目标。这些目标可能是固定的也可能是移动的，随着目标进入和离开目标区域，目标的数量可能会随时间变化。机器人配备的传感器具有有限的视野，并且可能会遇到假阴性和假阳性检测。机器人使用概率假设密度（PHD）过滤器的新颖，分布式公式（考虑了传感器的局限性）来估计目标的数量和目标的位置。然后，机器人使用劳埃德（Lloyd's）算法（已证明对覆盖和搜索任务有效）的分布式控制算法在环境中驱动其运动。我们将PHD滤波器的输出用作劳埃德算法中的重要性加权函数。这会使机器人被拉向可能包含目标的区域。我们通过一系列广泛的模拟实验证明了我们提出的算法的有效性，包括与具有统一重要性加权函数的基于覆盖率的控制器进行比较。这些实验表明，由10-100个机器人组成的团队成功地在2D和3D环境中跟踪了10-50个目标。通过一系列广泛的模拟实验。这些实验表明，由10-100个机器人组成的团队成功地在2D和3D环境中跟踪了10-50个目标。通过一系列广泛的模拟实验。这些实验表明，由10-100个机器人组成的团队成功地在2D和3D环境中跟踪了10-50个目标。