Enhancing Robotic Autonomy: A Review and Case Study of Traditional and Deep Learning Approaches to Inverse Kinematics

Inverse kinematics (IK) is a fundamental concept in robotics, essential for calculating the 
necessary joint angles to achieve a desired position and orientation of an end-effector in robotic arms and other 
articulated systems. Traditional methods of solving IK, such as analytical and numerical approaches, face 
significant challenges when addressing modern robotic applications' complexities and computational demands. 
This paper explores the integration of deep neural networks (DNNs) as a transformative approach to these IK 
challenges. DNNs offer a significant advantage in handling the nonlinearities inherent in robotic systems and 
provide a flexible framework for optimizing joint configurations with energy efficiency and obstacle avoidance 
considerations. The study presents a detailed comparison of traditional and neural network-based methods, 
highlighting the enhanced adaptability, efficiency, and robustness of neural networks. The paper discusses 
neural network architectures and their implementation for a 2D robotic arm. This advancement represents a 
pivotal shift from rigidity to flexibility in robotic motion planning and control, promising substantial 
improvements in robotic autonomy and functionality.