低空混合障碍下无人机通信终端自主着陆远程导航系统设计
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广州软件学院 软件与人工智能学院

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Design of Autonomous Landing Remote Navigation System for UAV Communication Terminal under Low altitude Mixed Obstacles
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    摘要:

    针对无人机在城郊工业园区、建筑群密集区等典型低空混合障碍场景下执行设备巡检、物资投送等任务时,面临的导航信号失锁、着陆精度失准等严峻工程难题,设计并实现了一套高可靠性的无人机通信终端自主着陆远程导航系统。本系统面向实际应用,硬件层面采用机载端-地面站协同架构,将支持4G/5G及专用无线数传的通信终端与GNSS模块分区隔离安装,并增设电磁屏蔽隔层,从物理隔离与电磁防护双维度规避干扰。定制化串口协议与STM32H743微处理器交互,临时存储通信中断时的关键定位数据,避免数据丢失导致的导航断连。地面数据接收中心配置工业级服务器、同频段无线数传接收模块及实时显示终端,确保数据传输稳定。在算法层面,融合PNP定位与扩展卡尔曼滤波的导航算法,结合视觉定位与多传感器数据融合,提升复杂环境下着陆精度与鲁棒性。实飞实验结果表明,该方法将滚转角、俯仰角、偏航角分别控制在[-1.5°-0.5°]、[-0.5°-0.5°]、[-2°-2°]范围内,均表现出最优的稳定性和精度,能有效抵抗低空障碍带来的干扰。并通过该方法能够精准控制无人机姿态,使得最终着陆点坐标与理想着陆点坐标一致,为(30m,4m,10m),能够满足无人机精准作业需求,为变电站设备巡检等低空任务提供可靠的导航技术支撑。

    Abstract:

    Aiming at the severe engineering challenges of navigation signal loss and inaccurate landing precision faced by unmanned aerial vehicles (UAVs) when performing tasks such as equipment inspection and material delivery in typical low-altitude mixed-obstacle scenarios like suburban industrial parks and dense building complexes, a highly reliable remote navigation system for UAV communication terminal autonomous landing has been designed and implemented. Oriented towards practical application, at the hardware level, the system adopts an airborne-ground station cooperative architecture. The communication terminal supporting 4G/5G and dedicated wireless data transmission is installed in separate zones from the GNSS module, with added electromagnetic shielding layers to avoid interference from both physical isolation and electromagnetic protection dimensions. A customized serial port protocol interacts with the STM32H743 microprocessor to temporarily store critical positioning data during communication interruptions, preventing navigation disconnection caused by data loss. The ground data reception center is equipped with an industrial-grade server, same-frequency band wireless data transmission reception modules, and a real-time display terminal to ensure stable data transmission. At the algorithm level, a navigation algorithm integrating PnP positioning and Extended Kalman Filtering, combined with visual positioning and multi-sensor data fusion, enhances landing accuracy and robustness in complex environments. Real-flight experimental results show that this method controls the roll angle, pitch angle, and yaw angle within the ranges of [-1.5°, 0.5°], [-0.5°, 0.5°], and [-2°, 2°], respectively, all demonstrating optimal stability and accuracy, and can effectively resist interference from low-altitude obstacles. Furthermore, this method enables precise control of the UAV attitude, resulting in the final landing point coordinates being consistent with the ideal landing point coordinates at (30m, 4m, 10m), meeting the requirements for precise UAV operations and providing reliable navigation technical support for low-altitude tasks such as substation equipment inspection.

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  • 收稿日期:2025-11-06
  • 最后修改日期:2025-12-17
  • 录用日期:2025-12-17
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