Title: Deep Learning for Model-Free Prediction of Thermal States of Robot Joint Motors Show Chinese title

Title: 基于深度学习的机器人关节电机热状态无模型预测

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. Show Chinese abstract

Abstract: 在这项工作中，由多个隐藏的长短期记忆（LSTM）和前馈层组成的深度神经网络被训练以预测机器人操作臂关节电机的热行为。采用了一种无模型且可扩展的方法。它能够应对由于大量近似模型参数的推导、识别和验证所带来的复杂性和不确定性问题，这些参数几乎不可获得。为此，收集并处理了感知的关节扭矩，以预见关节电机的热行为。展示了基于机器学习捕捉冗余机器人七个关节电机温度动态的有希望的预测结果。