Radioactive Source Seeking using Bayesian Optimisation with Movement Penalty
It addresses the sample inefficiency of gradient-based methods for mobile robotics in radiation safety, but results are only simulated.
This paper proposes a Bayesian optimisation strategy with a movement penalty for radioactive source seeking, achieving sublinear regret and effective source localisation in simulations.
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.