用神经网络加速不确定环境下目标搜索决策，计算量降阶但检测率相当。
Fast Neural-Network Approximation of Active Target Search Under Uncertainty
- 用卷积神经网络直接拟合主动搜索策略的决策过程。
- 在均匀和聚类分布下检测率与原方法相当，计算量降低数个数量级。
- 适合需要实时决策的移动机器人搜索任务，尤其对算力有限场景友好。
我们研究移动智能体在测量不确定性下搜寻未知数量、未知位置静止目标的问题。采用概率假设密度滤波器估计目标期望数量。现有规划方法如主动搜索（AS）及其间歇变体（ASI）虽能精准探测，但需昂贵的在线优化。为降低在线计算开销，我们提出使用卷积神经网络通过直接推理来近似AS或ASI的决策。网络基于AS/ASI生成的数据进行训练，输入为多通道网格，包含目标置信度、代理位置、访问历史及边界信息。在均匀和聚类目标分布的仿真中，该网络实现与AS/ASI相当的检测率，同时计算量降低数个数量级。
原文摘要 · Abstract (English)
We address the problem of searching for an unknown number of stationary targets at unknown positions with a mobile agent. A probability hypothesis density filter is used to estimate the expected number of targets under measurement uncertainty. Existing planners, such as Active Search (AS) and its Intermittent variant (ASI), achieve accurate detection but require costly online optimization. To reduce online computation, we propose to use a convolutional neural network to approximate AS or ASI decisions through direct inference. The network is trained on AS/ASI data using a multi-channel grid that encodes target beliefs, the agent position, visitation history, and boundary information. Simulations with uniform and clustered target distributions show that the network achieves detection rates comparable to AS or ASI while reducing computation by orders of magnitude.
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