摘要
为实现稳定可靠的植保机器人视觉伺服控制,提出了一种基于语义分割网络的作物行特征检测方法。基于语义分割网络ESNet实现农田场景图像像素级带状区域检测,并利用最小二乘算法拟合得到每条行作物线特征;在此基础上通过设计一种主导航线提取算法获取导航路径,并利用卡尔曼滤波对主导航线几何参数进行平滑处理,有效抑制了不平整地面导致的机器人运动颠簸与视觉图像测量噪声引起的导航参数波动。继而构建机器人前轮转向、后轮差速的阿克曼运动学模型;在图像空间坐标下设计纯追踪控制器实现植保机器人的伺服运动控制。大田环境下的现场实验结果为:总体横向偏差为0.092 m,验证了本文方法的有效性。
A crop line feature detection method based on semantic segmentation network was proposed to realize stable and reliable visual servo control of plant protection robot.Based on the semantic segmentation network which was termed with ESNet,pixel-wise labeling in farmland images was performed for ribbon regions detection,and least mean squares algorithm was utilized to find out all the crop line feature parameters in real time.Among the derived candidate lines features,a key route line was chosen as the valid navigation path which was responsible for subsequent robot motion control.Kalman filter was subsequently employed to smooth geometrical parameters of the previously specified key route,which effectively suppressed the fluctuation of navigation parameters caused by jolt behavior of plant protection robot generated from uneven ground and measurement noises incorporated in visual images.Afterwards,the sophisticated Ackermann steering kinematic model which was characterized by robot front-wheel steering and rear-wheel differential was introduced.A pure tracking controller was designed in Cartesian coordinate system to realize the servo motion control of plant protection robot.The field experiment conducted in real farmland scenarios verified the effectiveness of the proposed method.
作者
李秀智
方会敏
朱玉垒
杜博文
董泓佑
LI Xiuzhi;FANG Huimin;ZHU Yulei;DU Bowen;DONG Hongyou(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;School of Agricultural Engineering,Jiangsu University,Zhenjiang 212013,China;Key Laboratory of Modern Agricultural Equipment and Technology(Jiangsu University),Ministry of Education,Zhenjiang 212013,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2024年第5期21-27,39,共8页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(52005310)
江苏大学高级人才基金项目(22JDG041)
农业农村部现代农业装备重点实验室开放项目(2020007)。
关键词
植保机器人
视觉伺服控制
深度学习
语义分割网络
作物行特征检测
plant protection robot
visual servo control
deep learning
semantic segmentation net
crop line feature detection
