Adaptive quadrotor coverage search framework: enhancing UAV safety and efficiency in complex environments via multi-layer control and risk-aware planning

• Published in: Measurement science & technology Vol. 36; no. 7; p. 76316
• Main Authors: Lu, Xin, Zhang, Tao, Xing, Yang
• Format: Journal Article
• Language: English
• Published: 31.07.2025
• ISSN: 0957-0233; 1361-6501
• DOI: 10.1088/1361-6501/adecb5

Due to its simple power structure and flexible three-dimensional space maneuverability, quadrotor is increasingly used in area coverage search, image stitching, industrial mapping, forestry surveying, and agricultural drug spraying, and so on. In these scenarios, it is important to ensure both efficiency and safety to avoid falling into local optimization in the complex region of obstacles. In this paper, we propose a robust and efficient coverage search framework for quadrotors, aiming to significantly improve the flight safety and environmental adaptability of large unmanned aerial vehicles (UAVs) in complex environments. The back-end of the framework employs the minimum control effort trajectory optimization to optimize the temporal and spatial parameters of the motion trajectories to ensure the efficient navigation and obstacle avoidance performance of the UAV in complex environments. The main contributions of this paper focus on the front-end design, including: a global trajectory generation method based on the rotating calipers method for quickly determining the measurement area boundary and calculating the optimal flight belt direction and inclination; a dynamic and safe flight corridor generation method based on the environmental information and UAV geometry; a speed adaptive adjustment method based on the environmental risk assessment; and the use of a multi-layered model predictive contouring control framework to realize the transition from global path planning to local trajectory optimization multi-level optimization strategies. The flight safety of the framework in narrow passages and sudden obstacle environments, as well as the adaptive flight capability in open areas and dense obstacle areas are verified through extensive simulation and real flight experiments. The experimental results show that compared with existing UAV planning frameworks, this framework substantially improves the task efficiency in medium and high-speed scenarios, with average and maximum speedups of up to 89.15% and 124.29% in the ultra-high-speed case, and a reduction of up to 60% in the total task time, demonstrating higher adaptability and robustness.