ICCK Transactions on Intelligent Cyber-Physical Systems
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TY - JOUR AU - Zhou, Hui PY - 2026 DA - 2026/02/19 TI - Design and Kinematic Simulation Analysis of a Vision-Guided 5-DOF Robotic Arm System JO - ICCK Transactions on Intelligent Cyber-Physical Systems T2 - ICCK Transactions on Intelligent Cyber-Physical Systems JF - ICCK Transactions on Intelligent Cyber-Physical Systems VL - 1 IS - 1 SP - 51 EP - 59 DO - 10.62762/TICPS.2026.895157 UR - https://www.icck.org/article/abs/TICPS.2026.895157 KW - machine vision KW - 5-DOF robotic arm KW - STM32 microcontroller KW - trajectory planning KW - kinematic modeling AB - Traditional teach-and-repeat robotic arms, which rely on pre-programmed trajectories and manual teaching, struggle to adapt to operational requirements in unstructured environments where the position, posture, or type of target objects may vary unpredictably. In contrast, robot control technology based on visual servoing empowers robotic arms with "visual perception" capabilities, enabling real-time environment sensing and dynamic action adjustment, which significantly enhances the flexibility and efficiency of sorting and assembly operations. This paper designs and implements a five-degree-of-freedom (5-DOF) vision-guided picking robotic arm system based on a distributed control architecture, utilizing MATLAB as the upper computer for high-level decision-making and image processing, and an STM32 microcontroller as the lower computer for real-time motor control and communication. The research specifically addresses the key technical challenges of achieving motion smoothness and positioning accuracy under low-cost hardware conditions, where traditional control methods often fall short due to mechanical limitations and sensor inaccuracies. To this end, a quintic polynomial interpolation algorithm is employed for joint-space trajectory planning, ensuring continuous velocity and acceleration profiles and effectively mitigating the mechanical shock and jitter inherent in low-cost stepper motors and servos. Furthermore, a linear regression error compensation model is proposed to correct the systematic positioning errors caused by lens distortion and mechanical flex in monocular vision-based target localization, achieving high-precision 3D coordinate calculation without the need for expensive depth sensors. Experimental results demonstrate that the proposed system achieves smooth and reliable motion control, reduces average positioning error to within ±3 mm, and attains a 90\% success rate in autonomous grasping tasks, validating its practical value for automated sorting applications in cost-sensitive scenarios. SN - pending PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Zhou2026Design,
author = {Hui Zhou},
title = {Design and Kinematic Simulation Analysis of a Vision-Guided 5-DOF Robotic Arm System},
journal = {ICCK Transactions on Intelligent Cyber-Physical Systems},
year = {2026},
volume = {1},
number = {1},
pages = {51-59},
doi = {10.62762/TICPS.2026.895157},
url = {https://www.icck.org/article/abs/TICPS.2026.895157},
abstract = {Traditional teach-and-repeat robotic arms, which rely on pre-programmed trajectories and manual teaching, struggle to adapt to operational requirements in unstructured environments where the position, posture, or type of target objects may vary unpredictably. In contrast, robot control technology based on visual servoing empowers robotic arms with "visual perception" capabilities, enabling real-time environment sensing and dynamic action adjustment, which significantly enhances the flexibility and efficiency of sorting and assembly operations. This paper designs and implements a five-degree-of-freedom (5-DOF) vision-guided picking robotic arm system based on a distributed control architecture, utilizing MATLAB as the upper computer for high-level decision-making and image processing, and an STM32 microcontroller as the lower computer for real-time motor control and communication. The research specifically addresses the key technical challenges of achieving motion smoothness and positioning accuracy under low-cost hardware conditions, where traditional control methods often fall short due to mechanical limitations and sensor inaccuracies. To this end, a quintic polynomial interpolation algorithm is employed for joint-space trajectory planning, ensuring continuous velocity and acceleration profiles and effectively mitigating the mechanical shock and jitter inherent in low-cost stepper motors and servos. Furthermore, a linear regression error compensation model is proposed to correct the systematic positioning errors caused by lens distortion and mechanical flex in monocular vision-based target localization, achieving high-precision 3D coordinate calculation without the need for expensive depth sensors. Experimental results demonstrate that the proposed system achieves smooth and reliable motion control, reduces average positioning error to within ±3 mm, and attains a 90\\% success rate in autonomous grasping tasks, validating its practical value for automated sorting applications in cost-sensitive scenarios.},
keywords = {machine vision, 5-DOF robotic arm, STM32 microcontroller, trajectory planning, kinematic modeling},
issn = {pending},
publisher = {Institute of Central Computation and Knowledge}
}
ICCK Transactions on Intelligent Cyber-Physical Systems
ISSN: pending (Online)
Email: [email protected]
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