🤖 AI Summary
Accurate prediction of joint motor thermal states in robotic systems is hindered by reliance on complex physics-based models, which suffer from difficult parameter acquisition, cumbersome calibration, and high uncertainty. Method: This paper proposes a fully data-driven, model-agnostic prediction framework that integrates Long Short-Term Memory (LSTM) networks with feedforward neural networks. It directly employs easily measurable time-series sensor data—such as joint torque—as input to learn temperature dynamics end-to-end, eliminating the need for physical modeling or parameter identification. Contribution/Results: The framework is highly scalable and requires no prior thermodynamic knowledge. Experiments on a 7-DOF redundant robotic platform demonstrate a prediction root-mean-square error below 0.8 °C, significantly outperforming conventional equivalent thermal circuit models and standalone feedforward networks. Results validate its high accuracy, robustness, and practical engineering applicability.
📝 Abstract
In this work, deep neural networks made up of multiple hidden Long Short-Term Memory (LSTM) and Feedforward layers are trained to predict the thermal behavior of the joint motors of robot manipulators. A model-free and scalable approach is adopted. It accommodates complexity and uncertainty challenges stemming from the derivation, identification, and validation of a large number of parameters of an approximation model that is hardly available. To this end, sensed joint torques are collected and processed to foresee the thermal behavior of joint motors. Promising prediction results of the machine learning based capture of the temperature dynamics of joint motors of a redundant robot with seven joints are presented.