A comprehensive review of inverse kinematics techniques from analytical foundations to artificial intelligence integration

Inverse kinematics (IK) is crucial for enabling precise and coordinated motion in robotic systems. This review provides a comprehensive analysis of IK techniques in robotics, examining the traditional analytical, numerical methods and the transformative impact of artificial intelligence (AI)-based approaches. It explains IK fundamentals such as forward kinematics (FK) and joint angles, emphasising the analytical clarity of traditional methods and the adaptability of numerical IK. The evolution of AI-based IK, using machine learning (ML) and neural networks (NNs), is highlighted for its versatility and optimisation in various robotic applications. A comparative analysis outlines the strengths and limitations of traditional and AI-based IK methods, highlighting their efficiency in different scenarios. As robotics advances, the fusion of these IK techniques becomes crucial for navigating complexities and enhancing capabilities. The review underscores the symbiotic integration of classical, numerical, and AI-based IK techniques to meet the demands of modern robotics, fostering improved performance.