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Adaptive ergodic surveillance control of UAV against multiple unknown targets
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DOI：10.1007/s11431-025-3386-6.png)
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Abstract
En 中文
This paper investigates the problem of ergodic surveillance using a unmanned aerial vehicle (UAV) against multiple targets whose total number and positions are unknown. An adaptive ergodic control framework is proposed to achieve efficient and accurate surveillance. First, an improved Gaussian mixture probability hypothesis density (GM-PHD) filter is introduced to estimate the target density distribution based on local measurements obtained by an onboard sensor with limited perception range. Then, an exploration-exploitation balancing mechanism is designed to efficiently explore the unknown target distribution and achieve accurate ergodic surveillance. Finally, the heat equation driven active coverage (HEDAC) method is employed to generate ergodic trajectories based on the expected spatial density distribution. The proposed framework is lightweight, and its effectiveness and advantages are demonstrated through simulations and real-world experiments.
Keywords:
UAV surveillance
adaptive control
ergodic control
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