Junlin Xie
Papers
1
Total Citations
6
H-Index
1
About
Junlin Xie’s research centers on agricultural robotics and intelligent path planning, with a particular focus on automating fruit harvesting in complex, dynamic environments. His most cited work, “Simulation of Apple Picking Path Planning Based On Artificial Potential Field Method” (2019, 6 citations), addresses a critical challenge in precision agriculture: designing efficient, obstacle-aware trajectories for robotic manipulators. Xie’s contribution lies in adapting the artificial potential field algorithm to handle both obstacle avoidance and non-obstacle scenarios, enabling smoother and more reliable picking motions in unstructured orchard settings. This work has practical implications for reducing crop damage and improving harvest efficiency. While his citation count reflects an emerging career, Xie’s research is notable for bridging theoretical control methods with real-world agricultural applications—a field of growing importance as labor shortages drive automation. His approach demonstrates how classical robotics techniques can be tailored to biological environments, offering a foundation for future studies in soft robotics and sensor-guided harvesting. For students and researchers, Xie’s work exemplifies the iterative process of adapting established algorithms to niche, high-impact problems in agri-tech.
Research Focus
Key Achievements
Top Papers
- 1