SUMMARY

https://gatech.zoom.us/j/2762902346?pwd=M0pkOXB4TThMYXNCd3Zla1lVSDFyZz09 Radiological search and mapping are two separate problems that are currently performed by human operators in the field. These tasks could not only be more effective when performed by robotic agents, doing so would also keep human operators from being exposed to gamma radiation. Radiological mapping is the process of taking measurements to build an understanding of the contamination of an area as quickly as possible. This usually implies some degree of coverage for a predefined area. Radiological search is a similar problem that focuses on inferring what the parameters of a source of emissions might be and localizing them as quickly as possible. While a variety of techniques exist for both of these problems, they often have limitations that would prohibit effective and practical deployment. This work has two goals. The first is to improve current mapping methods. This is done by using information driven search with a novel configuration of air and ground robots equipped with counting instruments. The improvements gained are quantified with Monte Carlo simulations. The information driven method will be compared to the same configuration of robots performing random sampling and a configuration of ground robots performing a systematic rectilinear search. A linear reduction in mapping error with time is observed for the systematic search while exponential reductions are observed for the teams using both air and ground robots. The information driven search demonstrates the quickest reduction of mapping error with time. The second goal is to address the search problem with a refined particle filtering algorithm for localizing, identifying, and characterizing point sources of gamma radiation in the presence of obstacles. The proposed algorithm has five major improvements over the current state of the art. Firstly, it uses discrete precomputed attenuation kernels to perform radiation transport thousands of times per second. Secondly, it uses an introspective algorithm to dynamically adjust computational load to balance speed and accuracy. Thirdly it uses a gamma spectrum unfolding algorithm to incorporate spectral data. Fourthly, it uses multiple parallel particle filters for each isotope of interest, thus tailoring the attenuation kernels to the appropriate isotope. Finally, it performs all likelihood calculations in the logarithmic domain to improve robustness and accuracy. The overall methodology is evaluated with Monte Carlo simulations and lab scale results using live sources of gamma radiation. The results show vast reductions in computational burden for embedded hardware, increased search speed, and reduced error.