Bingkun Yuan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Bingkun Yuan has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Chunyang Liu
College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
1 shared publication - Guiju Fan
College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China
1 shared publication - Hongjian Zhang
College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
1 shared publication - Jinxing Wang
College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
1 shared publication - Linlin Sun
College of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China
1 shared publication - Linlong Jing
College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China
1 shared publication
Latest Publications
- Open Access
- 5 May 2025
- Published by MDPI AG in Agriengineering
- Vol. 7 (5) , 135
Design and Experimental Evaluation of a Two-Stage Domain-Segmented Harvesting Device for Densely Planted Dwarf Apple Orchards
To address the challenges of manual apple harvesting and the limitations of existing devices—such as constrained workspace, low efficiency, and limited flexibility—a two-stage, sub-region harvesting device was developed. The design, informed by the fruit distribution characteristics in densely planted dwarf apple orchards, integrates a positioning mechanism and a fruit-picking mechanism, enabling multiple pickings within a single positioning operation to enhance workspace coverage. A forward kinematics model was established using the Denavit–Hartenberg (D–H) parameter method. An improved Monte Carlo simulation based on a hybrid Beta distribution estimated the maximum reachable distances of the fruit-picking reference point in the X, Y, and Z directions as 2146 mm, 2169 mm, and 2165 mm, respectively—adequately covering the target harvesting domain. Incorporating a translational axis structure further expanded the harvesting volume by 1.165 m3, a 42.40% improvement over the conventional 3R configuration. To support adaptive control, a random point–geometry fusion method was proposed to solve for joint variables based on harvesting postures, and an automatic fruit-picking control system was implemented. Experimental validation, including reference point tracking and harvesting tests, demonstrated maximum positioning errors of 1.5 mm and 2.2 mm, a fruit-picking success rate of 76.53%, and an average picking time of 7.24 s per fruit—marking a 4.6% improvement compared to existing devices reported in previous studies. This study provides a comprehensive technical framework and practical reference for advancing mechanized apple harvesting.
- 5 May 2025
- Published by MDPI AG in Agriengineering
- Vol. 7 (5) , 135
Open AccessDesign and Experimental Evaluation of a Two-Stage Domain-Segmented Harvesting Device for Densely Planted Dwarf Apple Orchards
To address the challenges of manual apple harvesting and the limitations of existing devices—such as constrained workspace, low efficiency, and limited flexibility—a two-stage, sub-region harvesting device was developed. The design, informed by the fruit distribution characteristics in densely planted dwarf apple orchards, integrates a positioning mechanism and a fruit-picking mechanism, enabling multiple pickings within a single positioning operation to enhance workspace coverage. A forward kinematics model was established using the Denavit–Hartenberg (D–H) parameter method. An improved Monte Carlo simulation based on a hybrid Beta distribution estimated the maximum reachable distances of the fruit-picking reference point in the X, Y, and Z directions as 2146 mm, 2169 mm, and 2165 mm, respectively—adequately covering the target harvesting domain. Incorporating a translational axis structure further expanded the harvesting volume by 1.165 m3, a 42.40% improvement over the conventional 3R configuration. To support adaptive control, a random point–geometry fusion method was proposed to solve for joint variables based on harvesting postures, and an automatic fruit-picking control system was implemented. Experimental validation, including reference point tracking and harvesting tests, demonstrated maximum positioning errors of 1.5 mm and 2.2 mm, a fruit-picking success rate of 76.53%, and an average picking time of 7.24 s per fruit—marking a 4.6% improvement compared to existing devices reported in previous studies. This study provides a comprehensive technical framework and practical reference for advancing mechanized apple harvesting.