Ground Penetrating Radar (GPR) is a tool for mapping the subsurface in a non-invasive way. GPR surveys are currently carried out manually; a time-consuming, tedious and sometimes dangerous task. This report presents the high-level software components for an autonomous unmanned ground vehicle to conduct GPR surveys. The hardware system is a four-wheel drive, skid steering, battery operated vehicle with integrated GPR equipment. Autonomous surveys are conducted using lidar-inertial odometry with robust path planning, path following and obstacle avoidance capabilities. Evaluation shows that the vehicle is able to autonomously execute a planned survey with high accuracy and stops before collisions occur. This system enables high-frequency surveys to monitor the evolution of an area over time, allows one operator to monitor multiple surveys at once, and facilitates future research into novel survey patterns that are difficult to follow manually

We present robust explicit construction of 3D configuration spaces using controlled linear perturbation. The input is two planar parts: a fixed set and a moving set, where each set is bounded by circle segments. The configuration space is the three-dimensional space of Euclidean transformation (translations plus rotations) of the moving set relative to the fixed set. The goal of constructing the 3D configuration space is to determine the boundary representation of the free space where the intersection of the moving set and fixed set is empty. To construct the configuration space, we use the controlled linear perturbation algorithm. The controlled linear perturbation algorithm assigns function signs that are correct for a nearly minimal input perturbation. The output of the algorithm is a consistent set of function signs. This approach is algorithm-independent, and the overhead over traditional floating point methods is reasonable. If the fixed and moving sets are computer representations of physical objects, then computing the configuration space greatly aids in many computational geometry problems. The main focus of computing the configuration space is for the path planning problem. We must find if a path exists from the start to the goal, where the fixed set is the obstacle, and the moving set is the object trying to reach the goal.