用深度学习预测机器人关节电机的温度变化，无需建立复杂物理模型。
Deep Learning for Model-Free Prediction of Thermal States of Robot Joint Motors
- 采用多层LSTM和全连接网络，基于关节力矩数据预测温度动态。
- 对7自由度冗余机器人实现高精度温度预测，验证了方法的有效性。
- 适合缺乏精确参数建模条件下的机器人热管理场景。
本文利用由多个隐藏LSTM和前馈层组成的深度神经网络，训练以预测机器人机械臂关节电机的热行为。采用一种无模型且可扩展的方法，应对因大量近似模型参数难以获取而带来的建模、辨识与验证复杂性及不确定性挑战。为此，收集并处理关节力矩传感数据，以预判关节电机的热状态。针对具有七个关节的冗余机器人，展示了基于机器学习捕捉关节电机温度动态的优异预测结果。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。