2025
1.
Dawood Ahmed; Basit Muhammad Imran; Martin Churuvija; Manoj Karkee
An Integrated Visual Servoing Framework for Precise Robotic Pruning Operations in Modern Commercial Orchard Journal Article
In: IFAC-PapersOnLine, vol. 59, no. 23, pp. 28–33, 2025, ISSN: 2405-8963.
Abstract | Links | BibTeX | Tags: agricultural automation, agricultural robotics, perception, precision pruning, robotic manipulator, sensing, visual servoing
@article{ahmed_integrated_2025,
title = {An Integrated Visual Servoing Framework for Precise Robotic Pruning Operations in Modern Commercial Orchard},
author = {Dawood Ahmed and Basit Muhammad Imran and Martin Churuvija and Manoj Karkee},
url = {https://www.sciencedirect.com/science/article/pii/S2405896325024565},
doi = {https://doi.org/10.1016/j.ifacol.2025.11.758},
issn = {2405-8963},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {IFAC-PapersOnLine},
volume = {59},
number = {23},
pages = {28\textendash33},
abstract = {This study presents a vision-guided robotic control system for automated fruit tree pruning applications. Traditional pruning practices are labor-intensive and limit agricultural efficiency and scalability, highlighting the need for advanced automation. A key challenge is the precise, robust positioning of the cutting tool in complex orchard environments, where dense branches and occlusions make target access difficult. To address this, an Intel RealSense D435 camera is mounted on the flange of a UR5e robotic arm and CoTracker3, a transformer-based point tracker, is utilized for visual servoing control that centers tracked points in the camera view. The system integrates proportional control with iterative inverse kinematics to achieve precise end-effector positioning. The system was validated in Gazebo simulation, achieving a 77.77% success rate within 5mm positional tolerance and 100% success rate within 10mm tolerance, with a mean end-effector error of 4.28 ± 1.36 mm. The vision controller demonstrated robust performance across diverse target positions within the pixel workspace. The results validate the effectiveness of integrating vision-based tracking with kinematic control for precision agricultural tasks. Future work will focus on real-world implementation and the integration of force sensing for actual cutting operations.},
keywords = {agricultural automation, agricultural robotics, perception, precision pruning, robotic manipulator, sensing, visual servoing},
pubstate = {published},
tppubtype = {article}
}
This study presents a vision-guided robotic control system for automated fruit tree pruning applications. Traditional pruning practices are labor-intensive and limit agricultural efficiency and scalability, highlighting the need for advanced automation. A key challenge is the precise, robust positioning of the cutting tool in complex orchard environments, where dense branches and occlusions make target access difficult. To address this, an Intel RealSense D435 camera is mounted on the flange of a UR5e robotic arm and CoTracker3, a transformer-based point tracker, is utilized for visual servoing control that centers tracked points in the camera view. The system integrates proportional control with iterative inverse kinematics to achieve precise end-effector positioning. The system was validated in Gazebo simulation, achieving a 77.77% success rate within 5mm positional tolerance and 100% success rate within 10mm tolerance, with a mean end-effector error of 4.28 ± 1.36 mm. The vision controller demonstrated robust performance across diverse target positions within the pixel workspace. The results validate the effectiveness of integrating vision-based tracking with kinematic control for precision agricultural tasks. Future work will focus on real-world implementation and the integration of force sensing for actual cutting operations.
