AI RESEARCH
Fast Neural-Network Approximation of Active Target Search Under Uncertainty
arXiv CS.LG
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ArXi:2604.22254v1 Announce Type: new 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.