3D Path Planning for Radiation Scanning of Cargo Containers

Every year, the ports of entry of the continental United States receive millions of containers from container ships for processing. These containers contain everything that the country imports, and sometimes regulated items can be hidden inside them in attempt to smuggle them illegally into the country. Some of these items may be radioactive material meant for criminal purposes and represent a threat to national security. The containers are currently being scanned for radioactivity as they leave the port, but before leaving the port, containers can sit inside the port for weeks. It can be beneficial to scan these containers before they are picked up to catch the illegal material sooner and reduce the risk of danger to those nearby. Uncrewed Aerial Systems can be useful for scanning container stacks in container fields since they can be attached with sensors and reach heights that are difficult for humans. They can also scan autonomously, requiring less over watch from people. This thesis attempts to solve the problem of autonomous search by using an initial 3D scan of the search area to input into a 3D path planning algorithm to generate a flight path that will sufficiently scan the search area while minimizing flight time. Coverage is a main area of concern, as well is computational complexity and time. In order to maintain security of the aircraft, the path must be generated on-board the aircraft, and as such use on-board, lightweight, computers. The approach taken in this thesis is by breaking the problem down into 2D layers, and then developing paths on each layer based on where the obstacles are. In order to maximize coverage, contours are generated around the obstacles. The vertices of the contours are then treated like points to visit in a Travelling Salesman Problem. To incentivize paths that run alongside the obstacles for better radiation detection, paths that do not run close to the obstacles are given a higher cost than those that do, resulting in a cost-minimizing path planning algorithm yielding paths that stay close to obstacles. The Travelling Salesman Problem algorithm then yields the most time effective path to cover the area while maintaining a distance healthy for radiation scanning from the obstacles.