Yunxiang Ye
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yunxiang Ye has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Fengle Zhu
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
1 shared publication - Keqiang Yu
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
1 shared publication - Xiaohui Wang
College of Mechanical and Electronic Engineering, Northwest A & F University, Xianyang 712100, China
1 shared publication - Yanru Zhao
College of Mechanical and Electronic Engineering, Northwest A & F University, Xianyang 712100, China
1 shared publication - Yibo Zhou
College of Mechanical and Electronic Engineering, Northwest A & F University, Xianyang 712100, China
1 shared publication - Zhijing Wang
College of Mechanical and Electronic Engineering, Northwest A & F University, Xianyang 712100, China
1 shared publication
Latest Publications
- Open Access
Rolling 2D Lidar-Based Navigation Line Extraction Method for Modern Orchard Automation
Autonomous navigation is key to improving efficiency and addressing labor shortages in the fruit industry. Semi-structured orchards, with straight tree rows, dense weeds, thick canopies, and varying light conditions, pose challenges for tree identification and navigation line extraction. Traditional 3D lidars suffer from a narrow vertical FoV, sparse point clouds, and high costs. Furthermore, most lidar-based tree-row-detection algorithms struggle to extract high-quality navigation lines in scenarios with thin trunks and dense foliage occlusion. To address these challenges, we developed a 3D perception system using a servo motor to control the rolling motion of a 2D lidar, constructing 3D point clouds with a wide vertical FoV and high resolution. In addition, a method for trunk feature point extraction and tree row line detection for autonomous navigation has been proposed, based on trunk geometric features and RANSAC. Outdoor tests demonstrate the system’s effectiveness. At speeds of 0.2 m/s and 0.5 m/s, the average distance errors are 0.023 m and 0.016 m, respectively, while the average angular errors are 0.272° and 0.146°. This low-cost solution overcomes traditional lidar-based navigation method limitations, making it promising for autonomous navigation in semi-structured orchards.
Open AccessRolling 2D Lidar-Based Navigation Line Extraction Method for Modern Orchard Automation
Autonomous navigation is key to improving efficiency and addressing labor shortages in the fruit industry. Semi-structured orchards, with straight tree rows, dense weeds, thick canopies, and varying light conditions, pose challenges for tree identification and navigation line extraction. Traditional 3D lidars suffer from a narrow vertical FoV, sparse point clouds, and high costs. Furthermore, most lidar-based tree-row-detection algorithms struggle to extract high-quality navigation lines in scenarios with thin trunks and dense foliage occlusion. To address these challenges, we developed a 3D perception system using a servo motor to control the rolling motion of a 2D lidar, constructing 3D point clouds with a wide vertical FoV and high resolution. In addition, a method for trunk feature point extraction and tree row line detection for autonomous navigation has been proposed, based on trunk geometric features and RANSAC. Outdoor tests demonstrate the system’s effectiveness. At speeds of 0.2 m/s and 0.5 m/s, the average distance errors are 0.023 m and 0.016 m, respectively, while the average angular errors are 0.272° and 0.146°. This low-cost solution overcomes traditional lidar-based navigation method limitations, making it promising for autonomous navigation in semi-structured orchards.