Robust Adversarial Reinforcement Learning for Optimal Assembly Sequence Definition in a Cobot Workcell

Alessio, Alessandro; Aliev, Khurshid; Antonelli, Dario

doi:10.1007/978-3-030-99310-8_3

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Reinforcement Learning (RL) has also been widely used in cobot controllers to optimize training and improve performance. As it can be seen in Appendix Table 6, recently some online scheduling studies used reinforcement learning such as Alessio et al, (2022). Additionally, Liu et al, (2022b), and Zhang et al, (2022b) used reinforcement learning to optimize an assembly line balancing problem.…”

Section: Ml\rl In Human Pose Prediction and Task Schedulingmentioning

confidence: 99%

Collaborative robots in manufacturing and assembly systems: literature review and future research agenda

et al. 2023

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Nowadays, considering the constant changes in customers’ demands, manufacturing systems tend to move more and more towards customization while ensuring the expected reactivity. In addition, more attention is given to the human factors to, on the one hand, create opportunities for improving the work conditions such as safety and, on the other hand, reduce the risks brought by new technologies such as job cannibalization. Meanwhile, Industry 4.0 offers new ways to facilitate this change by enhancing human–machine interactions using Collaborative Robots (Cobots). Recent research studies have shown that cobots may bring numerous advantages to manufacturing systems, especially by improving their flexibility. This research investigates the impacts of the integration of cobots in the context of assembly and disassembly lines. For this purpose, a Systematic Literature Review (SLR) is performed. The existing contributions are classified on the basis of the subject of study, methodology, methodology, performance criteria, and type of Human-Cobot collaboration. Managerial insights are provided, and research perspectives are discussed.

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Section: Ml\rl In Human Pose Prediction and Task Schedulingmentioning

confidence: 99%

Collaborative robots in manufacturing and assembly systems: literature review and future research agenda

et al. 2023

View full text Add to dashboard Cite

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Collaborative Robotics: A Survey From Literature and Practitioners Perspectives

Montini,

Daniele,

Agbomemewa

et al. 2024

J Intell Robot Syst

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Collaborative robotics possesses the potential to revolutionise industrial automation by offering affordable and accessible solutions with reasonable skill requirements. However, identifying the most valuable and appropriate applications for this technology remains a challenge. This study conducted a comprehensive literature review to analyse the existing collaborative robotics applications, and the results showed that only a limited number of applications can be considered true collaboration, with even fewer classified as intelligent collaboration. The study comprised a survey designed to offer valuable insights to not only enhance the state-of-the-art analysis in the identification of existing challenges in the field of collaborative robotics but also to provide motivation to guide future advancements. By leveraging the survey results, researchers and practitioners will be better equipped to navigate the complex landscape of collaborative robotics and develop innovative solutions to tackle the identified challenges. This study also informs on the latest research and development in the field.

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Robust Adversarial Reinforcement Learning for Optimal Assembly Sequence Definition in a Cobot Workcell

Cited by 2 publications

References 13 publications

Collaborative robots in manufacturing and assembly systems: literature review and future research agenda

Collaborative robots in manufacturing and assembly systems: literature review and future research agenda

Collaborative Robotics: A Survey From Literature and Practitioners Perspectives

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