无人机在城市中搜寻移动目标，考虑遮挡影响并优化路径。
Occlusion-Aware Ground Target Search by a UAV in an Urban Environment
- 用概率可视体积建模遮挡，动态规划搜索路径
- 在复杂环境中提升搜索成功率，尤其当误报率高时
- 适合城市环境下的无人机实时目标追踪任务
本文研究在城市道路网络中，通过无人飞行器（UAV）搜索一个移动的目标点（POI）。UAV被建模为可变速率的杜宾斯车辆，配备视距传感器，其视野可能受城市环境遮挡。提出一种基于概率可视体积（VV）的搜索策略，利用迭代加深A*算法规划未来运动。概率可视体积是针对目标状态分布的时变三维感知约束表示。为寻找最可能观测到目标的路径，规划器采用启发式方法乐观估计时间窗内观测到目标的概率。通过最大值池化生成变步长规划器，缩小搜索空间并平衡长期与短期规划。在蒙特卡洛仿真中与基线方法对比，结果表明在杂乱环境中，当传感器误报率较高时，该方法显著优于现有方案。
原文摘要 · Abstract (English)
This paper considers the problem of searching for a point of interest (POI) moving along an urban road network with an uncrewed aerial vehicle (UAV). The UAV is modeled as a variable-speed Dubins vehicle with a line-of-sight sensor in an urban environment that may occlude the sensor's view of the POI. A search strategy is proposed that exploits a probabilistic visibility volume (VV) to plan its future motion with iterative deepening $A^\ast$. The probabilistic VV is a time-varying three-dimensional representation of the sensing constraints for a particular distribution of the POI's state. To find the path most likely to view the POI, the planner uses a heuristic to optimistically estimate the probability of viewing the POI over a time horizon. The probabilistic VV is max-pooled to create a variable-timestep planner that reduces the search space and balances long-term and short-term planning. The proposed path planning method is compared to prior work with a Monte-Carlo simulation and is shown to outperform the baseline methods in cluttered environments when the UAV's sensor has a higher false alarm probability.
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