Radioactive Source Seeking using Bayesian Optimisation with Movement Penalty

TL;DR

This paper introduces a Bayesian-Optimisation approach with a heteroscedastic Gaussian process and movement penalty for efficient radioactive source localization using mobile robots.

Contribution

It presents a novel, sample-efficient Bayesian-Optimisation method that incorporates movement costs to improve radioactive source seeking performance.

Findings

The method achieves sublinear regret in source seeking tasks.

Simulations confirm effective localization of radioactive sources.

The approach reduces unnecessary movement during source localization.

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.