3D Path Planning for a Rotary Wing UAV using a Gaussian Process Occupancy Map

This paper presents a 3D online path planning algorithm for a 6DOF Rotary Unmanned Aerial Vehicle (RUAV) operating in a cluttered environment using a Gaussian Process (GP) occupancy map. Traditional grid-based occupancy maps suffer from the curse of dimensionality for platforms that operate in a high dimensional configuration space. In addition, the grid resolutions, shapes and orientations limit the flexibility of the path planner. A GP occupancy map however performs inference directly on the collected sensor data, removing the need of maintaining a full discrete map. It performs inference about the probability of collision for any query point in continuous 3D space that potentially maximizes the flexibility of the path planner. We embed the GP map approach into our previous work on smooth path planning and control for an aerial vehicle and present results in a simulated cluttered environment.