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.
@article{arxiv.2605.14942,
title = {Radioactive Source Seeking using Bayesian Optimisation with Movement Penalty},
author = {Lysander Miller and Joshua Keene and Jeremy M. C. Brown and Airlie Chapman},
journal= {arXiv preprint arXiv:2605.14942},
year = {2026}
}