Load detection of industrial robots in manufacturing environment based on improved FNO network
Year: 2025
Type: article
Abstract: Introduction To tackle the insufficient accuracy in load detection of industrial robots, this study proposes a load detection approach based on a Fourier neural network. Methods First, 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. Results In 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. Discussion The proposed model shows good application effects and will provide technical support for parameter recognition and control optimization of industrial robots.
Authors: Fangyong Gao, Dechang Xie, Yu Xie
Institution: Guangxi Vocational & Technical Institute of Industry
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,418
Funder: Guangxi University
OpenAlex ID: W4415780785