Emergency UAV Landing on Unknown Field Using Depth-Enhanced Graph Structure

Jie Chen, Weiming Du, Junmou Lin, Uddin Md. Borhan, Yanning Lin, Bingqing Du, Xing Li, Jianqiang Li

Abstract: With the expanding use of Unmanned Aerial Vehicles (UAVs) in a variety of applications, their safety has become a critical concern. UAVs are confronted with a variety of unforeseen circumstances during missions; in these instances, the UAVs need to autonomously locate a suitable landing site, plan flight routes, and avoid obstacles in unstructured environments. Due to the limitations of computing power and sensors, it is challenging to attain the goal. The aim of research study is to investigate the monocular emergency autonomous landing algorithm. This work concentrates specifically on extracting depth and vision information. A topology information extractor is designed to transform images into graphs and assess the connectivity of terrain. In addition, a depth information extractor is designed to compute the slope and roughness of the ground. A 3D topology optimizer is designed to optimize the graph based on depth information and evaluate the landing suitability using a heuristic strategy. For action decision making, a 3D topology decision method based on Depth-Enhanced Graph Structure (DEGS) is proposed. In order to demonstrate the efficacy of DEGS, this study constructed a simulated scenario based on an actual scene. The results of the experiment indicate that DEGS outperforms its counterparts in terms of the accuracy of action prediction and landing success rate. Note to Practitioners—Using DEGS, this study proposes a novel method for emergency UAV landing on unknown fields. The proposed method is founded on the following fundamental concepts: First, the UAV first generates a DEGS of the unknown field. Second, the UAV then evaluates the landing risk and guides the UAVs in planning a safe landing trajectory. Third, the UAV implements the landing trajectory and lands safely on the unknown field using monocular aerial vision. Frame Sequential and Self-supervision Network (FSSN), a multi-scale vision-based Monocular Depth Estimation (MDE) network, is proposed to estimate the depth map for real-time UAV flight phases. This method has been evaluated using simulations and real-world dataset of simulation images of monocular continuous frames and has shown to be effective in landing UAVs on unknown fields. A human-in-the-loop learning approach is proposed for updating DEGS with dynamic terrain classification that made the procedure feasible for unknown fields. In terms of landing success rate and action prediction accuracy, the results demonstrate that the proposed method is capable of landing a UAV on unknown terrain in a safe and efficient manner, and it is particularly useful in emergency situations where the UAV does not have prior knowledge of the field.