Flexible scheduling and tactile communication for human–robot collaboration

Maderna, Riccardo; Pozzi, María; Zanchettin, Andrea Maria; Rocco, Paolo; Prattichizzo, Domenico

doi:10.1016/j.rcim.2021.102233

Cited by 25 publications

(3 citation statements)

References 35 publications

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“…Human-robot collaboration (HRC) is expected to add flexibility and agility to production lines in manufacturing plants. In this context, versatile scheduling algorithms are needed to organize the increasingly complex work-flow and to exploit the gained flexibility, ensuring the optimal use of resources and the smart management of failures (14) .…”

Section: Literaturementioning

confidence: 99%

Optimizing Cellular Manufacturing Systems Through Multi-Objective Cobot Coordination and Tool Allocation

Saleemuddin,

Hudgikar

2024

IJST

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Objectives: This study aims to enhance cellular manufacturing systems by optimizing cobot and tool assignments, maximizing flexibility, and minimizing production time, workload imbalances, energy consumption, error rates and rework. Methods: This study employs a sophisticated multi-objective optimization approach, integrating constraints into the cellular manufacturing system using advanced linear or integer programming techniques. The model is designed to dynamically adapt in real-time, allowing for flexibility in response to evolving production needs. We systematically evaluate cobot and tool assignments, balancing conflicting objectives within a comprehensive mathematical framework. The optimization process is fine-tuned to consider machine capacities, part type assignments, and tool compatibility, ensuring the practicality and realism of the proposed solutions. The overarching goal is to identify optimal configurations that minimize production time, workload imbalances, energy consumption, error rates and rework while maximizing system adaptability. Findings: The optimal cobot and tool assignments, determined through the multi-objective optimization model, yielded substantial improvements across critical metrics compared to a scenario without cobots. This data showcases a 26% reduction in production time, a 20% decrease in workload imbalance, a 20% improvement in flexibility, a 28% reduction in energy consumption, and a 26% decrease in error rates and rework when utilizing the proposed multi-objective optimization approach. These tangible improvements underscore the practical benefits of integrating cobots in cellular manufacturing systems.Top of Form Novelty: This study introduces a novel multi-objective optimization approach for cellular manufacturing, enhancing adaptability and efficiency through strategic cobot and tool assignments. Keywords: Cellular Manufacturing Systems, Cobots, Tool Assignment, MultiObjective Optimization, Production Time, Workload Balancing, Energy Consumption, Error Rates, Flexibility

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Section: Literaturementioning

confidence: 99%

Optimizing Cellular Manufacturing Systems Through Multi-Objective Cobot Coordination and Tool Allocation

Saleemuddin,

Hudgikar

2024

IJST

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“…Aside from this cluster, use-cases are highly individual. They include the assembly of desktop PCs [8], candy tins [224], emergency buttons [318], a filament winding head [241], car engine sub-assemblies [233], [317], pin-back buttons [240], USB adapters [320], or carbon fibre shells [243].…”

Section: B Tasks Used In Joint Action Experimentsmentioning

confidence: 99%

Benchmarking Teamwork of Humans and Cobots—An Overview of Metrics, Strategies, and Tasks

2023

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Human-robot teaming receives an ever-increasing level of attention in research, development and industry. Novel approaches to task sharing in hybrid teams range from optimized schedules to intelligent cobot assistants with a high degree of autonomy. These approaches must prove their usefulness and benefits compared to manual work or full automation -particularly when it comes to assessing their potential for productive industrial use. This leverages demand for standardized, repeatable benchmarks to compare approaches and measure improvements in a reproducible way. Designing such benchmarks is challenging as numerous aspects, from safety considerations to human factors and team performance, must be considered. This survey seeks to contribute to the future development of benchmarks for the field of collaborative assembly, handling, and industrial cobot applications by giving a comprehensive overview of relevant metrics, evaluation strategies, and tasks for human-robot teams.

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“…6,7 Currently, IWDs cover a large number of wearable types such as wristbands, smart patches, e-textiles and wearable screens, for measuring physical or chemical signals. [8][9][10][11] The electronic sensors can be easily integrated into common processors that can continuously provide accurate and reliable information to the devices. 12 In order to make the interaction between the sensor and non-planar surface stronger, it is common to apply the sensor with excellent flexibility, stretchability, and sensitivity, which allows the sensor to fit the target under test better and maximize the test range, thus sensing external environmental parameters more flexibly.…”

Section: Introductionmentioning

confidence: 99%