移動ペナルティ付きベイズ最適化による放射性線源探索
Radioactive Source Seeking using Bayesian Optimisation with Movement Penalty
放射性線源を効率的に探索するため、不均一分散ガウス過程を用いたベイズ最適化戦略を提案し、移動コストを考慮して探索と活用のバランスを取る。シミュレーションで有効性を実証した。
著者: Lysander Miller, Joshua Keene, Jeremy M. C. Brown, Airlie Chapman
分類: physics.app-ph, eess.SY
原文アブストラクト
The use of mobile robotics in radioactive source seeking has become an important part of modern radiation-safety practices, supporting timely mitigation of contamination risks and helping protect public health. However, measuring radiation is often time-consuming, rendering traditional gradient-based source-seeking methods less effective due to lower sample efficiency. This paper proposes a sample-efficient Bayesian-Optimisation source-seeking strategy that utilises a heteroscedastic Gaussian process surrogate to balance exploration and exploitation. Excessive inter-sample travel is discouraged through a movement switching cost. The strategy is shown to generate sublinear regret in the source-seeking task, while simulations demonstrate its effectiveness in localising radioactive sources.