Altitude-Constrained Occlusion-Aware UAV Coverage Planning for Autonomous Wilderness Search and Rescue

Abstract

Autonomous Unoccupied Aerial Vehicles (UAVs) have transformed how search and rescue (SAR) missions operate, enabling rapid real-time video feedback over large and difficult-to-access areas. However, ensuring full search coverage in wilderness environments is challenging, and current UAV path planning solutions do not account for occlusion caused by complex terrains and dense vegetation. In wilderness settings, UAV operators often rely on manual control to mitigate the effects of occlusion, performing on-the-fly position adjustments to achieve less-occluded viewing angles. To address the challenge of autonomous occlusion-aware coverage path planning, this dissertation presents VWSGA+, a waypoint path planning algorithm that guarantees complete coverage in occluded environments. The practicality and theoretical guarantees of VWSGA+ are demonstrated in a full-scale simulation and real-world experiments, in which the VWSGA+ is compared against state-of-the-art methods and is shown to be superior in providing complete coverage using battery-efficient paths. This dissertation advances the state of autonomy for UAV-assisted wilderness SAR operations by producing high-confidence aerial search coverage paths, ultimately enhancing the search team’s ability to provide this life-saving service to society.