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Integrating machine vision-based row guidance with GPS and compass-based routing to achieve autonomous navigation for a rice field weeding robot
Precision Agriculture ( IF 6.2 ) Pub Date : 2019-11-16 , DOI: 10.1007/s11119-019-09697-z
Sabeethan Kanagasingham , Mongkol Ekpanyapong , Rachan Chaihan

Autonomous weeding robots are a productive and more sustainable solution over traditional, labor-intensive weed control practices such as chemical weeding that are harmful to the environment when used excessively. To achieve a fully autonomous weed control operation, the robots need to be precisely guided through the crop rows without damaging rice plants and they should be able to detect the end of the crop row and make turns to change rows. This research attempted to integrate GNSS, compass and machine vision into a rice field weeding robot to achieve fully autonomous navigation for the weeding operation. A novel crop row detection algorithm was developed to extract the four immediate rows spanned by a camera mounted at the front of the robot. The extracted rows were used to determine a guide-line that was used to precisely maneuver the robot along the crop rows with an accuracy of less than a hundred millimeters in variable circumstances such as weed intensity, growth stage of plants and weather conditions. The GNSS and compass were used for locating the robot within the field. A state-based control system was implemented to integrate the GNSS, compass and vision guidance to efficiently navigate the weeding robot through a pre-determined route that covers the entire field without damaging rice plants. The proposed system was experimentally determined to deliver good performance in low weed concentrations with an accuracy of less than 2.5° in heading compensation and an average deviation from the ideal path of 45.9 mm. Though this accuracy dropped when the weed concentration increased, the system was still able to navigate the robot without inflicting any serious damage to the plants.

中文翻译:

将基于机器视觉的行引导与 GPS 和基于罗盘的路由相结合,实现稻田除草机器人的自主导航

与传统的劳动密集型杂草控制实践(例如过度使用会对环境有害的化学除草)相比,自主除草机器人是一种高效且可持续的解决方案。为了实现完全自主的杂草控制操作,机器人需要在不损坏水稻的情况下精确引导穿过作物行,并且它们应该能够检测到作物行的末端并轮流更换行。本研究试图将 GNSS、指南针和机器视觉集成到稻田除草机器人中,以实现除草作业的完全自主导航。开发了一种新的作物行检测算法来提取安装在机器人前面的摄像头跨越的四个直接行。提取的行用于确定指导线,该指导线用于在杂草强度、植物生长阶段和天气条件等可变情况下沿作物行精确操纵机器人,精度小于 100 毫米。GNSS 和指南针用于在场地内定位机器人。实施了基于状态的控制系统以集成 GNSS、指南针和视觉引导,以通过覆盖整个田地的预定路线有效地导航除草机器人,而不会损坏水稻。实验表明,所提出的系统在低杂草浓度下具有良好的性能,航向补偿精度小于 2.5°,与理想路径的平均偏差为 45.9 毫米。虽然当杂草浓度增加时,准确度会下降,
更新日期:2019-11-16
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