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All Journal International Journal of Robotics and Control Systems
Tan, Yan Kai
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Control & Systems Engineering Electrical & Electronics Engineering

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Journal : International Journal of Robotics and Control Systems

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A Review of Advanced Force Torque Control Strategies for Precise Nut-to-Bolt Mating in Robotic Assembly Ting, Terence Sy Horng; Goh, Yeh Huann; Chin, Kar Mun; Tan, Yan Kai; Chiew, Tsung Heng; Ma, Ge; How, Chong Keat
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1604

Abstract

Achieving precise alignment in high-precision robotic assembly is critical, where even minor misalignments can cause significant issues. Various control strategies have been developed to tackle these challenges, including passive compliance control (PCC), active control (AC), and manual teaching method (MTC). While AC is valued for its real-time adaptability, PCC and MTC offer advantages in simpler, cost-effective applications.   This review evaluates these strategies, emphasizing the integration of AI and machine learning to address the limitations of traditional AC methods, such as spiral and tilt searches, which are rigid, slow, and computationally demanding, making them unsuitable for dynamic environments. Machine Learning (ML) and Artificial Intelligence (AI) offer data-driven improvements in performance and adaptability over time. Techniques like Linear Regression, Artificial Neural Networks (ANNs), and Reinforcement Learning (RL) are explored for enhancing precision and real-time adaptability in complex tasks. These AI methods are applied in real-world industries, such as automotive and electronics manufacturing. The review compares control strategies and AI techniques, analyzing trade-offs in accuracy, speed, computational efficiency, and cost. It also discusses future directions, including hybrid control systems, advanced sensor integration, and more sophisticated AI algorithms. Ethical and safety considerations are highlighted, particularly in industrial settings where reliability and human-robot interaction are critical. This comprehensive review demonstrates AI's potential to enhance precision, reduce manual intervention, and improve performance in high-precision robotic assembly while guiding the selection of appropriate methods for specific applications.
Efficient Vision-Guided Robotic System for Fastening Assembly Using YOLOv8 and Ellipse Detection in Industrial Settings Tan, Yan Kai; Chin, Kar Mun; Goh, Yeh Huann; Chiew, Tsung Heng; Ting, Terence Sy Horng; MA, Ge; How, Chong Keat
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1705

Abstract

The assembly of fastening components traditionally relies on labour-intensive human-machine collaboration, which incurs high costs. Existing methods often assume fixed positions or use markers for guidance, requiring extra effort to place and maintain them. This study aims to develop an intelligent control system for a vision-equipped robotic arm to autonomously assemble fastening components in industrial settings, enhancing flexibility and reducing labour costs. The system integrates object detection with edge and ellipse detection, alongside filtering techniques, to accurately locate the centres of the fastening components.  The key contribution is the system's ability to perform autonomous assembly without predefined positions, enhancing flexibility in varied environments. YOLOv8 is employed to detect the bolt and nut, followed by edge and ellipse detection to pinpoint centre coordinates. A depth camera and kinematic calculations enable accurate 3D positioning for pick-and-place tasks. Experimental results demonstrate the system’s high effectiveness, with less than 1% of targets undetected. Based on experiments conducted in randomly arranged conditions, the system demonstrated high effectiveness, achieving over 99% detection accuracy. It achieved an 87% average success rate for picking fastening components ranging from sizes M6 to M18, and a 90% success rate for precise placement. Additionally, the system demonstrated robustness across various component sizes, with a minor increase in orientation errors for smaller components, attributed to depth estimation challenges. Future work could explore alternative depth data collection methods to improve accuracy. These results confirm the reliability of the system in flexible assembly tasks, demonstrating its potential to reduce costs by minimising manual involvement in industrial settings.
Co-Authors Chiew, Tsung Heng Chin, Kar Mun Goh, Yeh Huann How, Chong Keat Ma, Ge Ting, Terence Sy Horng