Dynamic task reallocation in human-robot collaborative workshop based on online biotic fatigue detection

Li, Xinyu; Xu, Wenjun; Yao, Bitao; Ji, Zhenrui; Liu, Xuedong

doi:10.1109/case49997.2022.9926591

Cited by 4 publications

(5 citation statements)

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“…On first glance, task allocation is a component of designing a collaborative assembly line problem, so it is best categorized as part of the first class. However, new approaches to human-robot communication, such as optical sensors, augmented reality (AR), and motion prediction systems, are aimed at addressing the lack of cognitive ability of cobots (Li et al, 2022). Consequently, in addition to traditional task allocation, real-time task allocation or scheduling has become more popular.…”

Section: Task Allocationmentioning

confidence: 99%

“…Zhang et al, (2022a and Lanzoni et al (2022) have employed AI approaches in their studies. Li et al (2022) provides a model for calculating online fatigue in an assembly line and reducing fatigue levels by assigning tasks to cobots.…”

Section: Task Allocationmentioning

confidence: 99%

“…Gualtieri et al, (2020a) provided a guideline for designing a product that improves the productivity of the collaborative assembly lines. Xu et al (2021), Li et al, (2021c), Vieira et al (2022), Li et al (2022), and Keshvarparast et al (2022) developed a multi-objective mathematical model in which one of the objective functions was to improve productivity. Zhang et al, (2021b) applied a new metric, a combination of productivity and ergonomics, to design a collaborative assembly line.…”

Section: Productivitymentioning

confidence: 99%

“…Keshvarparast et al (2022), and Dalle Mura and Dini (2022), developed a multi-objective mathematical model that one of the objective functions was minimizing the total physical workload for each worker. Li et al (2022) considered fatigue in a collaborative assembly line balancing. Navas-Reascos et al, (2022a) evaluate physical strain and muscular activities in a collaborative assembly line and compare it with the previous manual assembly line.…”

Section: Ergonomicsmentioning

confidence: 99%

“…Only Realyvásquez-Vargas et al, 2019, andKaraulova et al, 2019 investigated the ergonomic indexes in a comparative case study and analysed the difference between manual assembly line and collaborative assembly line by an ergonomic point of view. Recent studies assume the ergonomic index for tasks assigned to Cobots equal to zero (Stecke & Mokhtarzadeh, 2022;Li et al, 2022;Dalle Mura & Dini, 2022). However, this assumption neglects the fact that Cobots can create a new ergonomic load on workers (Lacevic et al, 2022;Lanzoni et al, 2022;Mura and Dini, 2023).…”

Section: Ergonomic Assessment and Impact On Injury Reduction Ratementioning

confidence: 99%

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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: Task Allocationmentioning

confidence: 99%