Optimized inverse kinematics modeling and joint angle prediction for six-degree-of-freedom anthropomorphic robots with Explainable AI

Inverse kinematics, crucial in robotics, involves computing joint configurations to achieve specific end-effector positions and orientations. This task is particularly complex for six-degree-of-freedom (six-DoF) anthropomorphic robots due to complicated mathematical equations, nonlinear behaviours, multiple valid solutions, physical constraints, non-generalizability and computational demands. The primary contribution of this work is to address the complex inverse kinematics problem for six-DoF anthropomorphic robots through the systematic exploration of AI models. This study involves rigorous evaluation and Bayesian optimization for hyperparameter tuning to identify the optimal regressor, balancing both accuracy and computational efficiency. Utilizing five-fold cross-validation on a publicly available dataset, the selected model demonstrates exceptional performance in predicting six joint angles for end effector configuration, yielding an average mean square error of 1.934 × 10⁻³ to 3.522 × 10⁻³. Its computational efficiency, with a prediction time of approximately 1.25 ms per sample, makes it a practical choice. Additionally, the study employs Explainable AI, using SHAP (SHapley Additive exPlanations) analysis to gain an understanding of feature importance. This analysis not only enhances model interpretability but also reaffirms the efficacy in this challenging multi-input multi-output predictive task. This research advances state-of-the-art models and neural networks by prioritizing computational efficiency alongside accuracy—a critical yet often overlooked factor. Pioneering a significant advancement in anthropomorphic robot kinematics, it balances accuracy and efficiency, offering practical robotic automation solutions.