Towards real-time UAV path replanning based on photogrammetry and learning-based approaches

Abstract. Unmanned Aerial Vehicles (UAVs) have contributed to a wide range of applications, becoming faster and more sustainable nowadays. However, given the significant increase in the number of UAVs, concerns regarding operational safety have grown. Autonomous UAV path planning must ensure compliance with safety requirements. This study proposes a real-time path replanning method focused on ensuring compliance with regulations governing UAV operations. Considering no-fly zones (NFZs) defined by both static (buildings) and dynamic (people) obstacles, a low-cost and replicable solution was implemented in four main steps: 3D offline path planning using the A* algorithm and Digital Elevation Models; human detection in UAV imagery using the YOLO11m model; estimation of the person’s 3D coordinates using Monoplotting; and experiments of real-time path replanning. During flight execution, imagery acquired by the UAV is transmitted to a server and, if a person is detected, path replanning is performed. The replanned route is then sent to the UAV controller to be executed via an SDK-based application. For flights at reduced speeds, the proposed method demonstrated feasibility in a computational environment (replanning time of 2.79 s). Simulated flight execution using the DJI Mobile SDK was successful. However, when relying on data transmission over Wi-Fi, the replanning duration on a local server (17.96 s) remained unsuitable for real-time operations. As future work, alternative solutions should be explored to ensure real-time processing. Despite the challenges, this study contributes by validating the open and free DJI MSDK application for path execution in a simulated environment, integrated with a listener application.