A Hybrid Human-Robot Collaborative Environment for Recycling Electrical and Electronic Equipment

Axenopulos, Apostolos; Papadopoulos, Georgios Th.; Giakoumis, Dimitrios; Kostavelis, Ioannis; Papadimitriou, Alexis; Sillaurren, Sara; Bastida, Leire; Oguz, Ozgur S.; Wollherr, Dirk; Garnica, Eugenio; Vouloutsi, Vasiliki; Verschure, Paul F. M. J.; Tzovaras, Dimitrios; Daras, Petros

doi:10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00312

Cited by 11 publications

(8 citation statements)

References 12 publications

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“…In the context of the WEEE recycling factory, humans and robots have to interact with a variety of different objects and tools and realize many changing sequences of actions in order to successfully complete their tasks Axenopoulos et al (2019). Although each of the robotic components has its own built-in safety mechanisms and corresponding certified ISO safety measures, the interaction of all these elements together will require an additional layer of control that can adapt their behavior to the requirements of the hybrid recycling plant while following human-centered design principles.…”

Section: Socially Adaptive Safety Engine As An Allostatic Control Systemmentioning

confidence: 99%

“…The experimental setup consists of a specific spatial and technical configuration of an adaptive Collaborative Cell (aCell) designed for the collaborative disassembly of Waste Electrical and Electronic Equipment (WEEE) devices. The concept of an aCell represents an evolution in the way we approach task allocation in HRC Axenopoulos et al (2019). Traditional industrial HRI methodologies often focus on individual tasks within a single work cell, with the human and robot working in isolation on specific tasks.…”

Section: The Acell Experimental Setupmentioning

confidence: 99%

“…One prime example of such a setting has been the implementation of a hybrid human-robot system in a recycling plant for Waste Electrical and Electronic Equipment (WEEE) products under the umbrella of the EU-funded HR-Recycler project Axenopoulos et al (2019). This type of environment poses a unique set of challenges for human-robot interaction (HRI) and collaboration (HRC) Robla-Gómez et al (2017), Vysocky and Novak (2016).…”

Section: Introductionmentioning

confidence: 99%

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Socially adaptive cognitive architecture for human-robot collaboration in industrial settings

Freire,

Guerrero-Rosado,

Amil

et al. 2024

Front. Robot. AI

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This paper introduces DAC-HRC, a novel cognitive architecture designed to optimize human-robot collaboration (HRC) in industrial settings, particularly within the context of Industry 4.0. The architecture is grounded in the Distributed Adaptive Control theory and the principles of joint intentionality and interdependence, which are key to effective HRC. Joint intentionality refers to the shared goals and mutual understanding between a human and a robot, while interdependence emphasizes the reliance on each other’s capabilities to complete tasks. DAC-HRC is applied to a hybrid recycling plant for the disassembly and recycling of Waste Electrical and Electronic Equipment (WEEE) devices. The architecture incorporates several cognitive modules operating at different timescales and abstraction levels, fostering adaptive collaboration that is personalized to each human user. The effectiveness of DAC-HRC is demonstrated through several pilot studies, showcasing functionalities such as turn-taking interaction, personalized error-handling mechanisms, adaptive safety measures, and gesture-based communication. These features enhance human-robot collaboration in the recycling plant by promoting real-time robot adaptation to human needs and preferences. The DAC-HRC architecture aims to contribute to the development of a new HRC paradigm by paving the way for more seamless and efficient collaboration in Industry 4.0 by relying on socially adept cognitive architectures.

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Section: Socially Adaptive Safety Engine As An Allostatic Control Systemmentioning

confidence: 99%

Section: The Acell Experimental Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Socially adaptive cognitive architecture for human-robot collaboration in industrial settings

Freire,

Guerrero-Rosado,

Amil

et al. 2024

Front. Robot. AI

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“…To this aim, the author describes two systems for the disassembly of electronic products based on both robots and cobots, by evidencing how significant technological challenges are limiting the number of systems developed for product disassembly. From another side, Papadopoulos et al [6] proposed a new hybrid human-robot WEEE recycling plant. Here, automatic (robotic based) systems have been exploited to categorize, disassemble, and sort products and components.…”

Section: Cobots and Weee Disassembly Processesmentioning

confidence: 99%

Semi-automated PCB Disassembly Station

Galparoli

Caielli

Rosa

et al. 2021

New Business Models for the Reuse of Secondary Resources From WEEEs

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The main aim of the FENIX project is the development of new business models and industrial strategies for three novel supply chains in order to enable value-added product-services. Through a set of success stories coming from the application of circular economy principles in different industrial sectors, FENIX wants to demonstrate in practice the real benefits coming from its adoption. In addition, Key Enabling Technologies (KETs) will be integrated within the selected processes to improve the efficient recovery of secondary resources. In this sense, among the available KETs, the adoption of digital and advanced automated solutions allows companies to re-thinking their business strategies, trying to cope with even more severe environmental requirements. Among these technological solutions, the paradigm of Industry 4.0 (I4.0) is the most popular. I4.0 entails the development of a new concept of economic policy based on high-tech strategies and internet-connected technologies allowing the creation of added-value for organizations and society. Unlike the activities developed in T3.1, related to the development and implementation of simulation tools and models for the smartphones’ disassembly process optimization, here the attention has been spent in managing and optimizing a new semi-automated PCBs disassembly station. The disassembly of products is a key process in the treatment of Waste Electrical and Electronic Equipment. When performed efficiently, it enables the maximization of resources re-usage and a minimization of pollution. Within the I4.0 paradigm, collaborative robots (co-bots in short) can safely interact with humans and learn from them. This flexibility makes them suitable for supporting current CE practices, especially during disassembly and remanufacturing operations. D3.2 focuses on describing the semi-automated PCB disassembly process implemented at the POLIMI’s Industry 4.0 Lab, aiming to demonstrate in practice the benefits of exploiting I4.0 technologies in PCB disassembly processes. Results highlight how a semi-automated cell where operators and cobots works together can allow a better management of both repetitive and specific activities, the safe interaction of cobots with operators and the simple management of the high variability related with different kinds of PCBs.

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“…Axenopulos et al [12] have discussed a novel human- Our objective was to evaluate HRC for a plastic recycling setting, which was simplified to make its performance feasible in the laboratory. Our specific aim was to investigate the effect of human involvement on task performance and fluency for a recyclable item sorting task.…”

Section: Introductionmentioning

confidence: 99%

Effect of Human Involvement on Work Performance and Fluency in Human-Robot Collaboration for Recycling

Ramadurai,

Jeong

2022

Preprint

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Human-robot collaboration has significant potential in recycling due to the wide variation in the composition of recyclable products. Six participants performed a recyclable item sorting task collaborating with a robot arm equipped with a vision system. The effect of three different levels of human involvement or assistance to the robot (Level 1-occlusion removal; Level 2-optimal spacing; Level 3-optimal grip) on performance metrics such as robot accuracy, task time and subjective fluency were assessed. Results showed that human involvement had a remarkable impact on the robots accuracy, which increased with human involvement level. Mean accuracy values were 33.3% for Level 1, 69% for Level 2 and 100% for Level 3. The results imply that for sorting processes involving diverse materials that vary in size, shape, and composition, human assistance could improve the robots accuracy to a significant extent while also being cost-effective.

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A Hybrid Human-Robot Collaborative Environment for Recycling Electrical and Electronic Equipment

Cited by 11 publications

References 12 publications

Socially adaptive cognitive architecture for human-robot collaboration in industrial settings

Socially adaptive cognitive architecture for human-robot collaboration in industrial settings

Semi-automated PCB Disassembly Station

Effect of Human Involvement on Work Performance and Fluency in Human-Robot Collaboration for Recycling

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