Load detection of industrial robots in manufacturing environment based on improved FNO network - Gao, Fangyong; Xie, Dechang; Xie, Yu (Frontiers in Mechanical Engineering, 2025)
IntroductionTo tackle the insufficient accuracy in load detection of industrial robots, this study proposes a load detection approach based on a Fourier neural network.MethodsFirst, a robot dynamics model is constructed, and a Fourier neural operator is introduced to extract spatial physical information. In addition, an attention mechanism is integrated to enhance key load information and mitigate the influence of the external environment.ResultsIn the load detection experiment, the proposed model achieved the best prediction accuracy compared with similar models. For example, when the load was 2 kg, 2.5 kg, and 3 kg, the predicted loads were 2.0044 kg, 2.5102 kg, and 3.0190 kg, respectively. Moreover, the model exhibited excellent fusion error compensation performance: the average error in fusion after compensation was 0.82 ms, and the maximum delay time after error correction remained within 3.25%. In terms of single - sample inference time, the proposed model performed best (5.1 ms), which was better than that of similar techniques.DiscussionThe proposed model shows good application effects and will provide technical support for parameter recognition and control optimization of industrial robots.