Unified Inverse Kinematics Framework Based on Optimization and Neural Solvers

This work presents a generalized framework for solving the inverse kinematics problem of robotic manipulators by introducing two approaches: optimization-based and learning-based approaches within a unified architecture. The optimization-based inverse kinematic solver is formulated as a minimization problem of the error in localization of the end-effector. On the other hand, the artificial neural network’s inverse kinematic solver is presented as a subsequent operation to a given straightforward forward kinematics analysis. The two proposed formulations are applicable to robot manipulators with any synthesis and degrees of freedom. To validate the proposed solvers, a low-cost 6-DOF robotic platform was developed, including a host-embedded system that enables real-time interaction between a virtual environment and a physical manipulator. Different optimization algorithms and different learning algorithms were used and their effect was compared to evaluate the efficiency of the proposed framework. The two approaches were analyzed and compared with respect to accuracy, computational cost, and real-time suitability. Results show that optimization methods provide higher precision and require longer computation time, whereas the artificial neural network-based method achieves significantly faster responses with acceptable approximation error. Experimental validation demonstrates the robustness and practical applicability of the framework, which is recommended for high-degree-of-freedom manipulators where analytical and closed-form solutions do not exist.