Human-robot collaborative disassembly enabled by brainwaves and improved generative adversarial network

Human-robot collaboration (HRC) can greatly facilitate the disassembling processes of end-of-life (EoL) products. For the robot in HRC, an intuitive control function enabled by a brain-machine interface (BMI) and human brainwaves will be useful to support dynamic decision-making under various disassembly conditions. However, a major challenge in developing a BMI-enabled HRC disassembly system is that there are usually a limited number of brainwave signals available for system training. To address the challenge, a novel model, namely TCN-D2GAN, was designed in this study. The model was used to augment acquired brainwave signals and support the establishment of the BMI-enabled HRC disassembly system. The innovations of the TCN-D2GAN model come from the following aspects: (i) the strengths of a temporal convolutional network (TCN) and a dual discrimination generative adversarial network (D2GAN) were combined to accomplish high-performance signal augmentation, and (ii) new loss functions were defined in the model to avoid the use of hyperparameters, so the training effect of the model can be optimised. Based on two public electroencephalography (EEG) datasets, experiments for signal augmentation and system training were carried out. The TCN-D2GAN model was compared with several mainstream generative adversarial network (GAN) models. Results indicated that the TCN-D2GAN model exhibited better performance in terms of signal classification accuracy and training efficiency. Finally, disassembly experiments were presented to validate the applicability of the system.