Browsing by Author "Fletcher, Sarah"

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    Chapter 12: Putting people and robots together in manufacturing: are we ready?
    (Springer, 2019-05-06) Fletcher, Sarah; Teegan, Johnson; Larreina, Jon
    Traditionally, industrial robots have needed complete segregation from people in manufacturing environments to mitigate the significant risk of injury posed by their high operational speeds and heavy payloads. However, advances in technology now not only enable the application of smaller force-limited robotics for lighter industrial tasks but also wider collaborative deployment of large-scale robots. Such applications will be critical to future manufacturing but present a design and integration challenge as we do not yet know how closer proximity and interactions will impact on workers’ psychological safety and well-being. There is a need to define new ethical and safety standards for putting people and robots together in manufacturing, but to do this we need empirical data to identify requirements. This chapter provides a summary of the current state, explaining why the success of augmenting human–robot collaboration in manufacturing relies on better consideration of human requirements, and describing current research work in the European A4BLUE project to identify this knowledge. Initial findings confirm that ethical and psychological requirements that may be crucial to industrial human–robot applications are not yet being addressed in safety standards or by the manufacturing sector.
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    Dual arm co-manipulation architecture with enhanced human–robot communication for large part manipulation
    (MDPI, 2020-10-29) Ibarguren, Aitor; Eimontaite, Iveta; Outón, José Luis; Fletcher, Sarah
    The emergence of collaborative robotics has had a great impact on the development of robotic solutions for cooperative tasks nowadays carried out by humans, especially in industrial environments where robots can act as assistants to operators. Even so, the coordinated manipulation of large parts between robots and humans gives rise to many technical challenges, ranging from the coordination of both robotic arms to the human–robot information exchange. This paper presents a novel architecture for the execution of trajectory driven collaborative tasks, combining impedance control and trajectory coordination in the control loop, as well as adding mechanisms to provide effective robot-to-human feedback for a successful and satisfactory task completion. The obtained results demonstrate the validity of the proposed architecture as well as its suitability for the implementation of collaborative robotic systems.
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    A head mounted augmented reality design practice for maintenance assembly: toward meeting perceptual and cognitive needs of AR users
    (Elsevier, 2021-09-28) Ariansyah, Dedy; Erkoyuncu, John Ahmet; Eimontaite, Iveta; Johnson, Teegan; Oostveen, Anne-Marie; Fletcher, Sarah; Sharples, Sarah
    Head Mounted Display (HMD) based Augmented Reality (AR) is being increasingly used in manufacturing and maintenance. However, limited research has been done to understand user interaction with AR interfaces, which may lead to poor usability, risk of occupational hazards, and low acceptance of AR systems. This paper uses a theoretically-driven approach to interaction design to investigate the impact of different AR modalities in terms of information mode (i.e. video vs. 3D animation) and interaction modality (i.e. hand-gesture vs. voice command) on user performance, workload, eye gaze behaviours, and usability during a maintenance assembly task. The results show that different information modes have distinct impacts compared to paper-based maintenance, in particular, 3D animation led to a 14% improvement over the video instructions in task completion time. Moreover, insights from eye gaze behaviours such as number of fixations and transition between Areas of Interest (AOIs) revealed the differences in attention switching and task comprehension difficulty with the choice of AR modalities. While, subjective user perceptions highlight some ergonomic issues such as misguidance and overreliance, which must be considered and addressed from the joint cognitive systems’ (JCSs) perspective and in line with the predictions derived from the Multiple Resources Model.
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    Understanding human variability to improve manufacturing system design
    (Cranfield University, 2004) Fletcher, Sarah; Baines, Tim; Asch, Rachel
    The way in which a manufacturing system is designed is a crucial determinant of its operational efficiency and profitability. System design is, therefore, a major consideration for organisations. However, the efficacy of current system design techniques could be improved. Designers still do not often construct assembly line layouts that provide the high level of efficiency and flexibility that is required by today's highly competitive organisations. One aspect of system design processes that has been found particularly deficient is the way in which human labour is considered. Current system design practices tend to disregard that workers may vary as they perform production tasks and the impact that this may have on the assembly line. In particular, system designers appear to have little understanding of how production work may be affected by workers' personal attributes. The research presented in this thesis sought to identify the nature and impact of human work performance to inform design stage practices. In particular, the research aimed to establish the extent to which the personal attributes of workers influence variations in their performance of production tasks. The research involved parallel data collection studies in a real manufacturing system where workers' performance of production tasks and their personal attributes in respect of work-related attitudes were measured and analysed. Overall, this research did not nd evidence of relationships between the particular work- related attitudes and production task performance that were measured in the study. However, indications were found that suggest production task performance variations may be produced by interactions between task characteristics and workers personal attributes. This evidence is an important development in understanding worker behaviour and informing manufacturing system designers that their neglect of worker performance variation in design stage evaluations may be a major cause of current design weaknesses.
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    “We don’t need ergonomics anymore, we need psychology!” – The human analysis needed for human-robot collaboration
    (AHFE International, 2022-07-24) Fletcher, Sarah; Eimontaite, Iveta; Webb, Phil; Lohse, Niels
    Human labour has always been essential in manufacturing and, still, no machine or robot can replace innate human complex physical (dexterity) and cognitive (reasoning) skills. Understandably, industry has constantly sought new automation technologies and largely only concerned itself with physical health and safety issues to improve / maintain production processes, but these industrial engineering approaches have largely overshadowed our understanding of wider social and emotional issues that can also significantly impact on human-system performance and wellbeing. In the current climate, industrial automation is rapidly increasing and crucial to manufacturing competitiveness, and requires greater, closer human interaction. Consequently, people’s cognitive-affective abilities have never been more critical and there has never been a more important time to thoroughly understand them. Moreover, industrial engineers are themselves now more aware and interested in understanding how people can better perform tasks in collaboration with intelligent automation and robotics. This paper describes why industry is only now realising the need for psychology, how far research has advanced our knowledge, and how a major UK project is working to develop new human behaviour models to improve effectiveness in the design of human-robot interactions in modern production processes. As one recent anecdotal comment from a UK industrialist set out: “we don’t need ergonomics anymore – our industrial engineers can do that, we need psychology”!