Inverse Kinematics Implementation Techniques in Robotics

Abstract

Inverse kinematics is crucial for offering precision in controlling robotic mechanisms, making them versatile for intricate roles in fields like manufacturing, medical services, and digital animation. It also facilitates innovations in automation, efficiency, and safety, thereby boosting overall performance in diverse industries. This review aims to present an overview of how deep learning techniques are employed in inverse kinematics, targeting researchers seeking to explore different approaches in this field. When calculating the inverse kinematics using the traditional approach, the complexity and non-linearity of a high degree of freedom robotic systems can pose limitations and lead to suboptimal results. Comparing different models of deep learning, this review focuses on the potential of deep learning as a suitable alternative approach for solving inverse kinematic problems. Also, it provides guidelines for researchers in utilizing deep learning for inverse kinematics applications while emphasizing the ethical and societal implications that arise from these advancements. Further emphasis is on the significance of case studies, insights into real-world applications, the challenges encountered, and future directions for research. Overall, this review covers various aspects of deep learning models' implementation in inverse kinematics. It also informs them about the potential of these models in advancing the field of inverse kinematics, paving the way for more precise and adaptive robotic movements, improved human-robot interactions, and greater autonomy in a wide range of industries.