Robotic radiation mapping using modelling and probabilistic analysis of sparse data

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An approach for generating radiation intensity maps, comprised of a mobile robotic platform and an integrated radiation model, is presented, and its ability to generate accurate radiation maps in simulation studies as well as real-life exposure scenarios are investigated. The radiation intensity mapping approach described here consists of two stages. First, radiation intensity samples are collected using a radiation sensor mounted on a mobile robotic platform, reducing the risk of exposure to humans from an unknown radiation field. Next, these samples, which need only to be taken from a subsection of the entire area being mapped, are then used to calibrate a radiation model. This model is then used to predict the radiation intensity field throughout the rest of the area. In this thesis, the technical details of both the prototype mobile robotic platform and the mathematical models for the radiation and map generation algorithms are presented in depth. The performance of the approach is evaluated in simulation studies and experiments in the lab. The sensitivity of the performance of the platform to changes in the number and orientation of the locations where the robot gathers samples from to calibrate the model for the given exposure scenario is analyzed quantitatively. The results show that the developed system is effective at achieving the goal of generating radiation maps using sparse data.
Radiation mapping, Robotics, Markov Chain Monte Carlo