Robotics paper index
Radioactive Source Seeking using Bayesian Optimisation with Movement Penalty
One-line summary
A sample-efficient Bayesian optimization strategy utilizing movement penalties for effective radioactive source localization.
Engineering notes
This research highlights the potential of advanced optimization techniques in enhancing robotic systems for hazardous material detection, providing practical solutions in radiation safety operations. The movement penalty can be fine-tuned based on the operational context to further improve efficiency.
Chinese explanation / 中文解读
移动机器人在放射源探测中的应用已成为现代辐射安全实践的重要组成部分，帮助及时减轻污染风险，保护公众健康。传统的基于梯度的探测方法由于采样效率低而效果不佳。本文提出了一种采样效率高的贝叶斯优化探测策略，通过异方差高斯过程模型平衡探索与利用，并通过移动切换成本抑制过度的样本间移动，显示出在源探测任务中产生亚线性后悔的能力。
Original abstract
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.
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