<![CDATA[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. Â Â 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.]]>
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