Robotic assembly is a fundamental technique in a wide range of manufacturing processes. While the classical rule-based robotic assembly methods have reached an elevated level of maturity, they have limitations when it comes to handling complex tasks with a large variety of components, which significantly restricts their applications. Fortunately, in the past decade, Deep Learning and Artificial Intelligence have undergone transformative breakthroughs, showcasing remarkable success across various domains such as computer vision and natural language processing (NLP). These data-driven approaches have demonstrated great generalizability and have paved the way for significant advancements in robotic assembly by improving the robot capability of perception, planning and control to meet the requirement of increasing complexity and variety of assembly tasks. This monograph is intended for undergraduate and postgraduate students interested in robotic intelligent assembly, researchers studying robotic intelligent assembly algorithms, and electronic, mechanical, and computer engineers engaged in industrial robot-assisted assembly.

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