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Evaluation of Human-Robot Collaborative Assembly Task Allocation Plan

Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 737)

Abstract

In recent years, collaborative robots have been introduced into production systems as a new generation of industrial robots, sharing work with workers. Unlike industrial robots and workers in the past to work in isolation, collaborative robots and workers can often share work tasks and space. The introduction of these collaborative robots makes human-robot collaboration (HRC) more and more important, which is an emerging technology in the field of production systems. According to the characteristics of workers and collaborative robots, a measurement model of assembly complexity is first proposed. The assembly tasks are analyzed from the three aspects of process depth, operation process, and cognitive process, and the complexity of each assembly task is evaluated. The indicator of the machine-machine collaboration attribute measures each task to determine whether the task can be completed by a collaborative robot; finally, an assembly task allocation evaluation model is established, to ensure the assembly complexity of each worker’s task is balanced, so that the cognitive load assigned to the robot task is lower than the worker’s task, to maximize production efficiency and reduce the assembly system cost.

Keywords

  • HRC assembly
  • Assembly complexity
  • HRC attributes

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Acknowledgements

The authors would like to express appreciation to mentors in Shanghai University and Huayu-intelligent Equipment Technology Co., Ltd for their valuable comments and other helps. Thanks for the pillar program supported by Shanghai Science and Technology Committee of China (No. 19511105200).

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Correspondence to Zenggui Gao .

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© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Yuan, S. et al. (2021). Evaluation of Human-Robot Collaborative Assembly Task Allocation Plan. In: Wang, Y., Martinsen, K., Yu, T., Wang, K. (eds) Advanced Manufacturing and Automation X. IWAMA 2020. Lecture Notes in Electrical Engineering, vol 737. Springer, Singapore. https://doi.org/10.1007/978-981-33-6318-2_61

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