Industrial robots (IRs), as the core equipment of intelligent manufacturing, play increasingly important roles in various industrial scenarios such as assembly, welding, handling, and spraying, significantly improving production efficiency and product quality. The massive popularization and application of IRs have brought about a sharp increase in energy consumption (EC), and the modeling and optimization of EC is becoming imperative. In this paper, a general energy modeling network DM-PLM for serial IRs based on Dynamic Model (DM) and Power Loss Model (PLM) is proposed by integrating the prior knowledge of IR power composition and dynamic mechanism, enabling efficient and accurate modeling of dynamics, power, and EC under multi-load conditions. Considering the force transmission characteristics of serial robots, this paper proposes an improved bidirectional recurrent neural network (BiRNN) to model the joint dynamics. Additionally, a power loss model based on the ResNet convolutional neural network is employed. Experiments are carried out with a KUKA KR210 heavy-duty robot and a UR5 collaborative robot. The results show that the DM-PLM model incorporating the physical mechanism priors achieves 97 %, 98 %, and 99 % modeling accuracy in joint torques, total power, and EC for both robots under multi-load conditions. In addition, the proposed DM-PLM model is applied to the EC optimization of KUKA KR210 through trajectory planning, which achieves over 30 % EC reduction with the genetic algorithm, providing an effective approach to improving the energy efficiency of serial IRs.

中文翻译：

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集成物理机制先验的串行工业机器人通用能量建模网络


工业机器人作为智能制造的核心装备，在装配、焊接、搬运、喷涂等各种工业场景中发挥着越来越重要的作用，显着提高了生产效率和产品质量。 IR的大规模普及和应用带来了能源消耗（EC）的急剧增加，EC的建模和优化势在必行。本文结合IR功率组成和动态机制的先验知识，提出了一种基于动态模型（DM）和功率损耗模型（PLM）的串行IR通用能量建模网络DM-PLM，实现了高效、准确的建模多负载条件下的动态、功率和 EC。考虑到串联机器人的力传递特性，本文提出了一种改进的双向循环神经网络（BiRNN）来建模关节动力学。此外，还采用了基于ResNet卷积神经网络的功率损耗模型。实验使用KUKA KR210重型机器人和UR5协作机器人进行。结果表明，结合物理机制先验的 DM-PLM 模型在多负载条件下，两种机器人的关节扭矩、总功率和 EC 的建模精度分别达到 97%、98% 和 99%。此外，所提出的DM-PLM模型通过轨迹规划应用于KUKA KR210的EC优化，通过遗传算法实现了超过30%的EC降低，为提高串行IR的能源效率提供了有效途径。