Emergency Landing Spot Detection Algorithm for Unmanned Aerial Vehicles

Abstract

The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area’s roughness, and the spot’s slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations.

An Image Processing Approach for Real-Time Safety Assessment of Autonomous Drone Delivery

“…Real-time experiments validated the proposed strategy via implementation in a low-cost mobile phone application and a commercial drone [29]. Additionally, in 2021, Loureiro et al [30] investigated the steps of detecting safe landing zones by developing an algorithm that classifies the (LIDAR) data and stores the positions of the most suitable zones. The geometric characteristics are used to discover the possible correct points, with Principal Component Analysis (PCA) then used to find the highest potential zone.…”

Evaluation of Runtime Monitoring for UAV Emergency Landing

“…The Core Model (CM) uses Semantic Segmentation to identify safe landing areas, while the Runtime Monitor (RM) is in charge of detecting CM errors to enhance the safety of the system. spots [4], [10]. To address this issue, a recent paper conducted a hazard analysis for urban EL, and used it to define specific requirements for EL in the style of the SORA [3].…”

“…EL consists in using either external databases or on-board sensors to identify a safe landing area, and to reach it in a potentially degraded control mode. This paper deals with the former sub-problem, sometimes called EL spot detection [4], which is simply referred to as EL from now on.…”

Suitable Landing Site Selection for Unmanned Aerial Vehicles Using Airborne Laser Scanning Point Cloud

“…This scholarly discourse is motivated by a commitment to address the complexities inherent in UAV landing site selection comprehensively. The overarching goal is to harness the capabilities of airborne laser scanning point cloud data to usher in a new era characterized by precision and autonomy in landing site selection for UAVs (Loureiro et al 2021) In the ensuing sections, this paper will navigate through contemporary techniques for analyzing point cloud data generated by airborne laser scanning. It will explore the inherent intricacies associated with UAV landing site selection, shedding light on challenges and promising avenues.…”