Guest Editors
Prof. Yangmin Li
Email: yangmin.li@polyu.edu.hk
Affiliation: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Hong Kong, China
Homepage:
Research Interests: manufacturing, automation, industrial engineering, robotics and AI
Dr. Jianguo Zhao
Email: jianguo.zhao@polyu.edu.hk
Affiliation: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Hong Kong, China
Homepage:
Research Interests: data-driven control, motion control
Dr. Zhenghan Zhu
Email: zhenghan.zhu@connect.polyu.hk
Affiliation: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Hong Kong, China
Homepage:
Research Interests: robotics and AI, nonlinear dynamics, data-driven modelling
Dr. Pengyuan Zhao
Email: zhaopengyuan@uestc.edu.cn
Affiliation: School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
Homepage:
Research Interests: intelligent robotics and systems
Summary
Smart manufacturing represents the next generation of industrial production paradigms, characterized by the deep integration of advanced digital technologies into every aspect of the manufacturing process. At the heart of this transformation lies the powerful synergy between robotics and artificial intelligence (AI). Leveraging AI technologies such as machine learning, deep learning, reinforcement learning, and computer vision, intelligent robotic systems are no longer merely automation tools, but have become adaptive, cognitive, and collaborative partners. This fusion enables unprecedented autonomy, flexibility, quality assurance, and system-level optimization, thereby driving the construction of resilient, self-optimizing, and efficient production environments. However, fully realizing the potential of smart manufacturing on an industrial scale remains a challenge, including data-driven decision-making, predictive optimization, human-machine collaboration, adaptive control, quality assurance, and integration with automated production environments.
This special issue focuses on the crucial roles of robotics and AI in realizing the vision of smart manufacturing. We aim to explore how to utilize intelligent robotic systems, AI technologies (including machine learning, deep learning, reinforcement learning, evolutionary computation, and heuristics), and intelligent sensing technologies to build smarter, more resilient, and self-optimizing smart manufacturing workflows. Furthermore, we are also interested in how these technologies can fundamentally reshape manufacturing workflows, encompassing all stages from design and planning to execution, monitoring, and maintenance.
This special issue provides a platform for researchers to share the latest theoretical advances, computational methods, and practical applications of robotic systems and artificial intelligence in smart manufacturing. Topics of interest include, but are not limited to:
· AI-driven robot perception, decision-making, and control
· Structural design, optimization, and control of intelligent robots
· Digital twins and AI for smart manufacturing
· External disturbance control in robot systems
· High-precision smart manufacturing methods
· Trajectory optimization and precision control of robotic systems
· Autonomous & adaptive smart manufacturing systems
· Human–robot collaboration and autonomous systems
· Computer vision and robotics applications in smart manufacturing
· Smart sensing, monitoring, and adaptive control
· Optimization algorithms in smart manufacturing
· Artificial intelligence-driven product quality inspection and optimization
· Heuristic and evolutionary optimization methods for complex smart manufacturing problems
Keywords
artificial intelligence, robotic system, intelligent robotics, machine learning, smart manufacturing, advanced manufacturing, smart systems, quality control, digital manufacturing, human-machine collaboration, reinforcement learning & adaptation
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