The rapid, accurate determination of the positions and strengths of sources of dangerous radioactivity takes high priority after a catastrophic event to ensure the safety of personnel, civilians, and emergency responders. This thesis presents approaches and algorithms to autonomously investigate radioactive material using an unmanned aerial vehicle.

Performing this autonomous analysis comprises five major steps: ingress from a base of operations to the danger zone, initial detection of radioactive material, measurement of the strength of radioactive emissions, analysis of the data to provide position and intensity estimates, and finally egress from the area of interest back to the launch site. In all five steps, time is of critical importance: faster responses promise potentially saved lives.

A time-optimal ingress and egress path planning method solves the first and last steps. Vehicle capabilities and instrument sensitivity inform the development of an efficient search path within the area of interest. Two algorithms—a grid-based recursive Bayesian estimator and a novel radiation contour analysis method—are presented to estimate the position of radioactive sources using simple gross gamma ray event count data from a nondirectional radiation detector. The latter procedure also correctly estimates the number of sources present and their intensities.

Ultimately, a complete unsupervised mission is developed, requiring minimal initial operator interaction, that provides accurate characterization of the radiation environment of an area of interest as quickly as reasonably possible.