面向药液自动补给的植保无人机着陆位置纠偏系统设计与试验

Design and Experiment of Landing Position Correction System for Plant Protection UAVs Aimed at Automatic Liquid Replenishment

摘要:

植保无人机具有较高的自动化作业程度，但药液补给环节严重依赖人工，亟需研发药液自动补给装置。受卫星定位误差、地形与瞬时横风等因素影响，植保无人机自主返航模式下着陆点与预设点之间往往存在偏差，无法与药液补给装置精准对接。本文提出一种基于视觉模块的植保无人机着陆位置纠偏系统，采用OV7725图像传感器采集植保无人机图像，基于色彩空间转换与腐蚀算法识别植保无人机，提取植保无人机边界与中心坐标；采用STM32微控制器作为核心处理单元，通过实时解算植保无人机中心坐标与预设着陆点之间的位置偏差，并基于PID控制算法输出相应脉冲信号至步进电机，驱动位置纠偏机构执行位移补偿，从而实现位置纠偏。试验结果表明，植保无人机识别精度为93.25%，平均定位误差为1.87cm。试验结果为植保无人机与药液自动补给装置精准对接、实现药液自动补给作业提供了技术支持与方法支撑。

Abstract:

Plant protection unmanned aerial vehicles (UAVs) have achieved a high degree of automation in spraying agricultural chemicals, yet the process of agricultural chemical replenishment remains largely dependent on manual operation, which limits overall operational efficiency. Factors such as global navigation satellite system (GNSS) positioning inaccuracies, terrain variations, and sudden crosswinds often cause significant deviations between the actual landing point and the predefined target when the UAV returns autonomously. These deviations hinder precise docking with an automatic replenishment device. To overcome this challenge, a vision-based landing position correction system was presented for plant protection UAVs’ automatic agricultural chemicals replenishment. The system employed an OV7725 complementary metal oxide semiconductor (CMOS) imaging sensor to capture real-time images during the UAV’s landing phase. A series of image processing steps were applied, including color space conversion from red green blue (RGB) to hue saturation lightness (HSL) to improve illumination invariance, threshold segmentation for preliminary detection, and a morphological erosion algorithm for accurate boundary extraction and center localization of the UAV. An STM32 microcontroller computed the positional offset between the detected UAV center and the desired landing coordinates. Experimental validation showed that the proposed system achieved a recognition accuracy of 93.25% for the plant protection UAV and attained an average positioning error of 1.87 cm. The results confirmed the system’s capability to enable accurate alignment between the UAV and an automated liquid replenishment unit. This research offered a viable and precise technical solution for automated chemical replenishment, contributing to enhanced operational intelligence and efficiency in precision aerial agriculture. The proposed approach demonstrated strong potential for practical implementation in modern agricultural aviation systems.