Reconstruction of radiation dose rate profiles by autonomous robot with active learning and Gaussian process regression

This work proposes an approach to use autonomous intelligent robots to reconstruct 2D dose rate profiles in radioactive environments. The main idea is to use Gaussian process (GP) regression to model the radiation dose rate distribution and active learning (AL) to dynamically improve estimation of GP parameters. A differential evolution (DE) algorithm, guided by the GP entropy is used to find the next measuring point to be visited by the robot in order to improve the GP estimation. Euclidean distances are used as penalizations to minimize the robot's trajectory and, consequently, to speed-up the reconstruction of the dose rate profile. The intelligent algorithm was tested through computational simulations considering 2 hypothetical scenarios with increasing complexity. Results demonstrate that the approach is able to reconstruct the dose rate profile with good accuracy and reduced number of measurements.