Α Behavior Trees-based architecture towards operation planning in hybrid manufacturing

Kokotinis, George; Michalos, George; Arkouli, Zoi; Makris, Sotiris

doi:10.1080/0951192x.2023.2228254

Cited by 5 publications

(1 citation statement)

References 44 publications

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“…Kokotinis et al proposed an approach for dynamic decision-making in optimum human–robot collaboration (HRC) to allocate multiple operations between multiple resources to assemble large-scale parts in the automotive case study. An algorithm tree-based architecture is used to estimate the cycle time criterion and calculate the total duration for task allocation (Kokotinis et al , 2023b).…”

Section: Introductionmentioning

confidence: 99%

Human-robot collaborative task planning for assembly system productivity enhancement

Inkulu,

Bahubalendruni

2024

RIA

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Purpose In the current era of Industry 4.0, the manufacturing industries are striving toward mass production with mass customization by considering human–robot collaboration. This study aims to propose the reconfiguration of assembly systems by incorporating multiple humans with robots using a human–robot task allocation (HRTA) to enhance productivity. Design/methodology/approach A human–robot task scheduling approach has been developed by considering task suitability, resource availability and resource selection through multicriteria optimization using the Linear Regression with Optimal Point and Minimum Distance Calculation algorithm. Using line-balancing techniques, the approach estimates the optimum number of resources required for assembly tasks operating by minimum idle time. Findings The task allocation schedule for a case study involving a punching press was solved using human–robot collaboration, and the approach incorporated the optimum number of appropriate resources to handle different types of proportion of resources. Originality/value This proposed work integrates the task allocation by human–robot collaboration and decrease the idle time of resource by integrating optimum number of resources.

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Section: Introductionmentioning

confidence: 99%

Human-robot collaborative task planning for assembly system productivity enhancement

Inkulu,

Bahubalendruni

2024

RIA

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AI Enhanced Processing of Large Parts: Volume Monitoring and Control of Robotic Gluing Application

Prezas,

Arkouli,

Michalos

et al. 2024

Lecture Notes in Mechanical Engineering

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Achieving productivity and operator well-being: a dynamic task allocation strategy for collaborative assembly systems in Industry 5.0

Calzavara,

Faccio,

Granata

et al. 2024

Int J Adv Manuf Technol

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Collaborative robots, or cobots, offer a unique combination of productivity and flexibility that has led to significant growth in adoption over the past decade. Moreover, recently, there has been a shift towards a human-centered design of the workspace, known as one of the drivers of Industry 5.0, which prioritizes the well-being of operators. To achieve this, various human factors such as ergonomics, mental workload, personal skills, and capabilities need to be considered in the workspace design, and their impact on system productivity must be evaluated. The integration of a human and a cobot in the same workplace can affect the performance of the human operator, as the perception of the cobot can impact their work. This highlights the importance of taking human factors into account, as a lack of consideration in these aspects has contributed to the failure of many implementations. To link the objectives of productivity, flexibility, and human factors consideration, a dynamic real-time multi-objective task allocation strategy for collaborative assembly systems is developed. This approach considers the different characteristics of the resources and optimizes for two objectives, makespan, and energy expenditure of the operator. By using this approach, it is possible to modify the behavior of the cobot by reallocating tasks between the two resources based on the operator’s current needs. In other words, if the operator appears too stressed due to time constraints or their energy rate level is too high, some of their assigned tasks can be transferred to the cobot. This helps to maintain a balanced system while reducing the operator’s stress.

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Α Behavior Trees-based architecture towards operation planning in hybrid manufacturing

Cited by 5 publications

References 44 publications

Human-robot collaborative task planning for assembly system productivity enhancement

Human-robot collaborative task planning for assembly system productivity enhancement

AI Enhanced Processing of Large Parts: Volume Monitoring and Control of Robotic Gluing Application

Achieving productivity and operator well-being: a dynamic task allocation strategy for collaborative assembly systems in Industry 5.0

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