Symbiotic human–robot collaborative approach for increased productivity and enhanced safety in the aerospace manufacturing industry

Pérez, Luís Álvarez; Rodríguez-Jiménez, Silvia; Rodríguez, Noel; Usamentiaga, Rubén; García, Daniel; Wang, Lihui

doi:10.1007/s00170-019-04638-6

Cited by 57 publications

(32 citation statements)

References 25 publications

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“…For example, most nal assemblies in the automotive and aircraft industry are dominantly completed by manual operations [5]. However, due to the inability to maximize productivity of manual assembly systems, there is a strong motivation to increase the level of automation in these domains [6]. This is intended to overcome weaknesses associated with human workers such as being susceptible to high workload, fatigue, and stress [4].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Human Sensor Framework for Human-Robot Collaboration

Buerkle

Matharu

Al-Yacoub

et al. 2021

Preprint

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Manufacturing challenges are increasing the demands for more agile and dexterous means of production. At the same time, these systems aim to maintain or even increase productivity. The challenges risen from these developments can be tackled through Human-Robot Collaboration (HRC). HRC requires effective task distribution according to each parties’ distinctive strengths, which is envisioned to generate synergetic effects. To enable a seamless collaboration, the human and robot require a mutual awareness, which is challenging, due to the human and robot “speaking” different languages as in analogue and digital. Thus, this challenge can be addressed by equipping the robot with a model of the human. Despite a range of models being available, data-driven models of the human are still at an early stage. This paper proposes an adaptive human sensor framework, which incorporates objective, subjective, and physiological metrics, as well as associated Machine Learning. Thus, it is envisioned to adapt to the uniqueness and dynamic nature of human behavior. To test the framework, a validation experiment was performed, including 18 participants, which aims to predict Perceived Workload during two scenarios, namely a manual and an HRC assembly task. Perceived Workloads are described to have a substantial impact on a human operator’s task performance. Throughout the experiment physiological data from an electroencephalogram (EEG), an electrocardiogram (ECG), and respiration sensor was collected and interpreted. For subjective metrics, the standardized NASA Task Load Index was used. Objective metrics included task completion time and number of errors/assistance requests. Overall, the framework revealed a promising potential towards an adaptive behavior, which is ultimately envisioned to enable a more effective HRC.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Human Sensor Framework for Human-Robot Collaboration

Buerkle

Matharu

Al-Yacoub

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Dispatching production, production orders, detailed scheduling, reliability, and availability assurance 4 Complexity architecture of human-robot systems including control, safety, and interface components [22]; security system frameworks to ensure the protection of data interaction during manufacturing [23]; and the exploration of approaches moving towards a cloud manufacturing ecosystem [24]. In summary, the requirements of horizontal integration are as follows:…”

Section: Levelmentioning

confidence: 99%

Industry 4.0‐Driven Development of Optimization Algorithms: A Systematic Overview

et al. 2021

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The Fourth Industrial Revolution means the digital transformation of production systems. Cyber-physical systems allow for the horizontal and vertical integration of these production systems as well as the exploitation of the benefits via optimization tools. This article reviews the impact of Industry 4.0 solutions concerning optimization tasks and optimization algorithms, in addition to the identification of the new R&D directions driven by new application options. The basic organizing principle of this overview of the literature is to explore the requirements of optimization tasks, which are needed to perform horizontal and vertical integration. This systematic review presents content from 900 articles on Industry 4.0 and optimization as well as 388 articles on Industry 4.0 and scheduling. It is our hope that this work can serve as a starting point for researchers and developers in the field.

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“…Robots are perfect substitutes for a skilled workforce for some repeatable, general, and strategically-important tasks, but this substitution is not straightforward [5]. The automation of an industrial manufacturing process raises some previous issues:…”

Section: Proposed Approachmentioning

confidence: 99%

“…The introduction of process automation and intelligent collaborative robots results in a rapid increase in productivity, major material and energy savings, and safer working conditions (repetitive and dangerous tasks can be done by robots). Robots provide versatility and flexibility; thus they are perfect substitutes for a skilled workforce for some repeatable, general, and strategically-important tasks [5]. Moreover, if robots' capacities are combined with humans' qualities, cost-effective productivity can be guaranteed [6].…”

Section: Introductionmentioning

confidence: 99%

Digital Twin and Virtual Reality Based Methodology for Multi-Robot Manufacturing Cell Commissioning

et al. 2020

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Intelligent automation, including robotics, is one of the current trends in the manufacturing industry in the context of “Industry 4.0”, where cyber-physical systems control the production at automated or semi-automated factories. Robots are perfect substitutes for a skilled workforce for some repeatable, general, and strategically-important tasks. However, this transformation is not always feasible and immediate, since certain technologies do not provide the required degree of flexibility. The introduction of collaborative robots in the industry permits the combination of the advantages of manual and automated production. In some processes, it is necessary to incorporate robots from different manufacturers, thus the design of these multi-robot systems is crucial to guarantee the maximum quality and efficiency. In this context, this paper presents a novel methodology for process automation design, enhanced implementation, and real-time monitoring in operation based on creating a digital twin of the manufacturing process with an immersive virtual reality interface to be used as a virtual testbed before the physical implementation. Moreover, it can be efficiently used for operator training, real-time monitoring, and feasibility studies of future optimizations. It has been validated in a use case which provides a solution for an assembly manufacturing process.

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Symbiotic human–robot collaborative approach for increased productivity and enhanced safety in the aerospace manufacturing industry

Cited by 57 publications

References 25 publications

An Adaptive Human Sensor Framework for Human-Robot Collaboration

An Adaptive Human Sensor Framework for Human-Robot Collaboration

Industry 4.0‐Driven Development of Optimization Algorithms: A Systematic Overview

Digital Twin and Virtual Reality Based Methodology for Multi-Robot Manufacturing Cell Commissioning

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