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Abstract Robotic technology can benefit disassembly operations by reducing human operators' workload and assisting them with handling hazardous materials. Safety consideration and prediction of the human movement are priorities in close collaboration between humans and robots. The point-by-point forecasting of human hand motion, which forecasts one point at each time, does not provide enough information on human movement due to errors between the actual movement and the predicted value. This study provides a range of possible hand movements to increase safety. It applies three machine learning techniques, including long short-term memory (LSTM), gated recurrent unit (GRU), and Bayesian neural network (BNN) combined with bagging and Monte Carlo dropout (MCD), namely, LSTM-bagging, GRU-bagging, and BNN-MCD to predict the possible movement range. The study uses an inertial measurement unit (IMU) dataset collected from the disassembly of desktop computers by several participants to show the application of the proposed method. 

Abstract This paper presents a deep learning enhanced adaptive unscented Kalman filter (UKF) for predicting human arm motion in the context of manufacturing. Unlike previous network-based methods that solely rely on captured human motion data, which is represented as bone vectors in this paper, we incorporate a human arm dynamic model into the motion prediction algorithm and use the UKF to iteratively forecast human arm motions. Specifically, a Lagrangian-mechanics-based physical model is employed to correlate arm motions with associated muscle forces. Then a Recurrent Neural Network (RNN) is integrated into the framework to predict future muscle forces, which are transferred back to future arm motions based on the dynamic model. Given the absence of measurement data for future human motions that can be input into the UKF to update the state, we integrate another RNN to directly predict human future motions and treat the prediction as surrogate measurement data fed into the UKF. A noteworthy aspect of this study involves the quantification of uncertainties associated with both the data-driven and physical models in one unified framework. These quantified uncertainties are used to dynamically adapt the measurement and process noises of the UKF over time. This adaption, driven by the uncertainties of the RNN models, addresses inaccuracies stemming from the data-driven model and mitigates discrepancies between the assumed and true physical models, ultimately enhancing the accuracy and robustness of our predictions. One unique point of our method is that it integrates a dynamic model of human arms and two RNN models, and uses Monte Carlo dropout sampling to quantify the uncertainties inherent in our RNN prediction models and transforms them into the covariances of the UKF’s measurement and process noises respectively. Compared to the traditional RNN-based prediction, our method demonstrates improved accuracy and robustness in extensive experimental validations of various types of human motions. 

With the advance of human-robot collaboration (HRC), collaborative robots (cobots) have emerged as solutions to alleviate the manual tasks involved in electronic waste (e-waste) disassembly. This study employed surface electromyography (EMG) to investigate whether cobots can enhance muscle coordination. EMG-EMG coherence in both beta and gamma bands was calculated from 22 participants to quantify coordination between four muscle groups—biceps brachii (BB), brachioradialis (BR), upper trapezius (UT), and erector spinae (ES). Comparison results showed that after the introduction of the cobot, significant increases in left BR&BB, BR&UT, BR&ES, and BB&UT pairs, right BR&BB, BR&UT, and BB&ES pairs, and bilateral BR pair were observed. Notably, left BR&ES presented the most substantial increase at 18.88% and 26.39% in the beta and gamma bands, respectively ( p < .05). These findings suggest that cobots hold potential to enhance muscle coordination during e-waste disassembly, thereby shedding light on the construction of HRC-based e-waste disassembly systems. 

Abstract Product disassembly plays a crucial role in the recycling, remanufacturing, and reuse of end-of-use (EoU) products. However, the current manual disassembly process is inefficient due to the complexity and variation of EoU products. While fully automating disassembly is not economically viable given the intricate nature of the task, there is potential in using human–robot collaboration (HRC) to enhance disassembly operations. HRC combines the flexibility and problem-solving abilities of humans with the precise repetition and handling of unsafe tasks by robots. Nevertheless, numerous challenges persist in technology, human workers, and remanufacturing work, which require comprehensive multidisciplinary research to address critical gaps. These challenges have motivated the authors to provide a detailed discussion on the opportunities and obstacles associated with introducing HRC to disassembly. In this regard, the authors have conducted a review of the recent progress in HRC disassembly and present the insights gained from this analysis from three distinct perspectives: technology, workers, and work. 

Robotic technology can benefit disassembly operations by reducing human operators’ workload and assisting them with handling hazardous materials. Safety consideration and predicting human movement is a priority in human-robot close collaboration. The point-by-point forecasting of human hand motion which forecasts one point at each time does not provide enough information on human movement due to errors between the actual movement and predicted value. This study provides a range of possible hand movements to enhance safety. It applies three machine learning techniques including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bayesian Neural Network (BNN) combined with Bagging and Monte Carlo Dropout (MCD), namely LSTM-Bagging, GRU-Bagging, and BNN-MCD to predict the possible movement range. The study uses an Inertial Measurement Units (IMU) dataset collected from the disassembly of desktop computers to show the application of the proposed method. The findings reveal that BNN-MCD outperforms other models in forecasting the range of possible hand movement.