Difficulty and complexity definitions for assembly task allocation and assignment in human–robot collaborations: A review

Kiyokawa, Takuya; Shirakura, Naoki; Wang, Zhenting; Yamanobe, Natsuki; Ramirez-Alpizar, Ixchel G.; Wan, Weiwei; Harada, Kensuke

doi:10.1016/j.rcim.2023.102598

Cited by 18 publications

(4 citation statements)

References 110 publications

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“…In the eld of HRC, task allocation has emerged as a pivotal area of study (Kiyokawa et al, 2023). The primary objective of task allocation approaches is to distribute assembly tasks between human workers and cobots (Ranz et al, 2017).…”

Section: Literature Review On Collaborative Task Allocationmentioning

confidence: 99%

Integrating Bio Principles in Assembly Task Design to Enhance Symbiotic Human-Robot Collaboration

Barravecchia,

Bartolomei,

Mastrogiacomo

et al. 2024

Preprint

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The advancement of Human-Robot Collaboration (HRC) in industrial environments has underscored the importance of establishing harmonious and symbiotic relationships between humans and robots. This study, in line with the principles of Industry 5.0, proposes an approach to support the integration of human capabilities with advanced robotics, enhancing collaborative productivity and fostering a paradigm shift towards a more interactive and beneficial human-robot symbiosis. Prior research has established the basic principles of Symbiotic Human-Robot Collaboration (SHRC), but has often neglected the critical problem of how to conduct collaborative tasks to exploit the potential of these symbiotic interactions. This paper presents a novel methodology to support the design of protocols for collaborative tasks, with the aim of promoting positive symbiotic interactions between humans and collaborative robots. The focus is on developing tasks that naturally integrate positive symbiotic interactions, which involves determining task performers and optimizing the mutual benefits derived from task execution. A case study is presented to illustrate the practical application of this methodology in a real-world context.

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Section: Literature Review On Collaborative Task Allocationmentioning

confidence: 99%

Integrating Bio Principles in Assembly Task Design to Enhance Symbiotic Human-Robot Collaboration

Barravecchia,

Bartolomei,

Mastrogiacomo

et al. 2024

Preprint

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“…Equation ( 8) represents the proportion of the process completed before the machine failure; equation (9) shows the remaining processing time of the production order on the faulty machine, depending on when the machine failure occurred; Equation (9) indicates the remaining processing time of the production order on the faulty machine, depending on when the machine failure occurred; equation (10) states that no processing occurs on the faulty machine during its failure period. Finally, equation (11) indicates the completion time of the production order on the faulty machine.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…First, the fitness of the parent chromosomes is calculated and sorted in ascending order by fitness. The total number of elite individuals to be selected from the parent population is calculated using Equation (9), where rand(0.7, 1) is a random number between 0.7 and 1. |P t + Q t | represents the total number of individuals in the parent population.…”

Section: Optimizing Production Order Scheduling Using a Genetic Algor...mentioning

confidence: 99%

“…These systems offer advantages such as easier access to updated drawings, the ability to quickly share changes or corrections, and improved collaboration among different parts of the manufacturing team. Reasonable task allocation methods can optimize the allocation of production resources, thereby significantly improving the economic and social benefits of enterprises [6][7][8][9]. With the increasing level of informatization and automation, the intelligent distribution of manufacturing drawings has become an inevitable trend.…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent Manufacturing Management System for Enhancing Production in Small-Scale Industries

Wang,

Cai,

Huang

et al. 2024

Electronics

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Industry 4.0 integrates the intelligent networking of machines and processes through advanced information and communication technologies (ICTs). Despite advancements, small mechanical manufacturing enterprises face significant challenges transitioning to ICT-supported Industry 4.0 models due to a lack of technical expertise and infrastructure. These enterprises commonly encounter variable production volumes, differing priorities in customer orders, and diverse production capacities across low-, medium-, and high-level outputs. Frequent issues with machine health, glitches, and major breakdowns further complicate optimizing production scheduling. This paper presents a novel production management approach that harnesses bio-inspired methods alongside Internet of Things (IoT) technology to address these challenges. This comprehensive approach integrates the real-time monitoring and intelligent production order distribution, leveraging advanced LoRa wireless communication technology. The system ensures efficient and concurrent data acquisition from multiple sensors, facilitating accurate and prompt capture, transmission, and storage of machine status data. The experimental results demonstrate significant improvements in data collection time and system responsiveness, enabling the timely detection and resolution of machine failures. Additionally, an enhanced genetic algorithm dynamically allocates tasks based on machine status, effectively reducing production completion time and machine idle time. Case studies in a screw manufacturing facility validate the practical applicability and effectiveness of the proposed system. The seamless integration of the scheduling algorithm with the real-time monitoring subsystem ensures a coordinated and efficient production process, ultimately enhancing productivity and resource utilization. The proposed system’s robustness and efficiency highlight its potential to revolutionize production management in small-scale manufacturing settings.

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Towards a Hybrid Intelligence Paradigm: Systematic Integration of Human and Artificial Capabilities

Mestre

2024

Lecture Notes in Business Information Processing

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Difficulty and complexity definitions for assembly task allocation and assignment in human–robot collaborations: A review

Cited by 18 publications

References 110 publications

Integrating Bio Principles in Assembly Task Design to Enhance Symbiotic Human-Robot Collaboration

Integrating Bio Principles in Assembly Task Design to Enhance Symbiotic Human-Robot Collaboration

An Intelligent Manufacturing Management System for Enhancing Production in Small-Scale Industries

Towards a Hybrid Intelligence Paradigm: Systematic Integration of Human and Artificial Capabilities

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