In recent years, there has been a trend to adopt human-robot collaboration (HRC) in the industry. In previous studies, computer vision-aided human pose reconstruction is applied to find the optimal position of point of operation in HRC that can reduce workers’ musculoskeletal disorder (MSD) risks due to awkward working postures. However, the reconstruction of human pose through computer-vision may fail due to the complexity of the workplace environment. In this study, we propose a data-driven method for optimizing the position of point of operation during HRC. A conditional variational auto-encoder (cVAE) model-based approach is adopted, which includes three steps. First, a cVAE model was trained using an open-access multimodal human posture dataset. After training, this model can output a simulated worker posture of which the hand position can reach a given position of point of operation. Next, an awkward posture score is calculated to evaluate MSD risks associated with the generated postures with a variety of positions of point of operation. The position of point of operation that is associated with a minimum awkward posture score is then selected for an HRC task. An experiment was conducted to validate the effectiveness of this method. According to the findings, the proposed method produced a point of operation position that was similar to the one chosen by participants through subjective selection, with an average difference of 4.5 cm.
This study aims to improve workers’ postures and thus reduce the risk of musculoskeletal disorders in human-robot collaboration by developing a novel model-free reinforcement learning method.
Human-robot collaboration has been a flourishing work configuration in recent years. Yet, it could lead to work-related musculoskeletal disorders if the collaborative tasks result in awkward postures for workers.
The proposed approach follows two steps: first, a 3D human skeleton reconstruction method was adopted to calculate workers’ continuous awkward posture (CAP) score; second, an online gradient-based reinforcement learning algorithm was designed to dynamically improve workers’ CAP score by adjusting the positions and orientations of the robot end effector.
In an empirical experiment, the proposed approach can significantly improve the CAP scores of the participants during a human-robot collaboration task when compared with the scenarios where robot and participants worked together at a fixed position or at the individual elbow height. The questionnaire outcomes also showed that the working posture resulted from the proposed approach was preferred by the participants.
The proposed model-free reinforcement learning method can learn the optimal worker postures without the need for specific biomechanical models. The data-driven nature of this method can make it adaptive to provide personalized optimal work posture.
The proposed method can be applied to improve the occupational safety in robot-implemented factories. Specifically, the personalized robot working positions and orientations can proactively reduce exposure to awkward postures that increase the risk of musculoskeletal disorders. The algorithm can also reactively protect workers by reducing the workload in specific joints.
- Award ID(s):
- 2024688
- NSF-PAR ID:
- 10415088
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Human Factors: The Journal of the Human Factors and Ergonomics Society
- Volume:
- 66
- Issue:
- 6
- ISSN:
- 0018-7208
- Format(s):
- Medium: X Size: p. 1754-1769
- Size(s):
- p. 1754-1769
- Sponsoring Org:
- National Science Foundation
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