Enhanced Safety in Industrial Robots: An Optimized Hybrid Method for External Torque Prediction

Accurate prediction of external torque in industrial robotic manipulators remains essential for ensuring precision, operational safety, and system adaptability. The present study proposes an optimized hybrid method that accurately estimates external torque using only joint-level data, addressing the limitations of conventional approaches that rely on additional torque sensors. The performed method is the support vector machine (SVM) and its parameters are optimized using particle swarm optimization (PSO). The proposed SVM-PSO is structured based only the signals of the position sensor that is existing at any industrial manipulator joint. The developed model utilizes three input parameters: the current joint position error, the previous joint position error, and the actual joint velocity. The experimental procedures involved acquiring data under controlled sinusoidal joint motion and some random collisions with the robot links, followed by statistical validation using Williams’ method to confirm the reliability of both training and testing datasets. The model demonstrated excellent performance, achieving a RMSE of 0.0985 and a R of 0.9952 during training. It maintained strong generalization performance during testing, with an RMSE of 0.1669 and a R of 0.986. The minimal discrepancy between estimated and actual external torque values affirms the method’s predictive reliability. When benchmarked against existing state-of-the-art methods, the proposed approach consistently outperformed competitors in both accuracy and robustness. The elimination of external torque sensors, coupled with the exclusive reliance on joint-level data, enables a practical and scalable framework suitable for real-time torque estimation in industrial robotic systems.