Deep Learning for Model-Free Prediction of Thermal States of Robot Joint Motors
This addresses thermal management for robot manipulators, but it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackles the problem of predicting thermal states in robot joint motors by using deep neural networks with LSTM and feedforward layers, achieving promising prediction results on a seven-joint redundant robot.
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.