Autonomous Landing Site Detection & Safe Approach Path Planning for Personal Aerial Mobility Vehicles

Abstract

As Personal Aerial Mobility (PAM) and Electric Vertical Take-off and Landing (eVTOL) aircraft are moving from just ideas to actual use, the reliance on fixed, pre-mapped infrastructure poses a significant barrier to global scalability. In regions with low infrastructure and harsh weather conditions such as Uzbekistan, the ability to perform autonomous, unplanned landings is critical [1]. This research suggests a solid framework for landing site selection and approach path generation, providing an innovative infrastructure for autonomous air mobility. The methodology utilizes a geometric filtering approach to process Digital Surface Models (DSM) and Digital Terrain Models (DTM), extracting slope and obstacle data to generate a Safety Score Map. This map serves as the cost-grid on A* search algorithm, ensuring a verifiable and optimal path to the safest identified landing zone [1][3]. Throughout the research several contributions like the development of a real-time autonomous scoring metric, dynamic route planning via grid-based optimization, and sensing analytics for trustworthy decision-making in unstructured environments is needed. Experimental evaluation within a high-fidelity simulation environment demonstrates that this simple geometric approach achieves high precision in obstacle avoidance and landing site accuracy, providing a transparent alternative to complex, black-box deep learning systems [1][2].