Dynamic safety system for collaboration of operators and industrial robots

Malm, Timo; Salmi, Timo; Marstio, Ilari; Montonen, Jari

doi:10.1515/eng-2019-0011

Cited by 22 publications

(9 citation statements)

References 7 publications

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“…A collaborative workspace should be configured at a distance that satisfies the ISO 13855 requirements. To assess the reliability of SMS, the collaborative robot should accurately detect the presence of a human and automatically restart when the human leaves the collaborative workspace (Belingardi et al, 2017; Malm et al, 2019; Marvel & Norcross, 2017). HG, the second level of collaborative operation, allows a human operator to easily guide and program the movements of a collaborative robot (Belingardi et al, 2017; Safeea & Neto, 2022; Vicentini, 2020).…”

Section: Resultsmentioning

confidence: 99%

Analysis of quality standards for industrial collaborative robots based on user‐centered design framework

Heo,

Putri,

Kim

et al. 2023

Hum Ftrs & Erg Mfg Svc

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Industrial collaborative robots have become increasingly important in recent years due to their ability to work safely and efficiently alongside humans. As a result, there is a growing need for evaluation standards to ensure the quality of collaborative robots. However, existing studies only consider system‐centered and technical aspects of collaborative robots, and there is a lack of research on user‐centered quality evaluation. In this study, we identified 21 user requirements based on a user‐centered design framework and confirmed the limitations of existing quality standards by reviewing the standard clauses for collaborative robots. It was found that user needs to be related to performance, safety, and even usability and enjoyment are already being expressed according to the user‐centered design framework, but the quality standards for these needs only present design principles or do not consider them at all. This study provides information on the quality attributes that need to be fulfilled to satisfy user requirements and suggests the need and direction for further research on the user‐centered evaluation of collaborative robots. Accordingly, the user's perception and experience of collaborative robots are expected to improve.

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Section: Resultsmentioning

confidence: 99%

Analysis of quality standards for industrial collaborative robots based on user‐centered design framework

Heo,

Putri,

Kim

et al. 2023

Hum Ftrs & Erg Mfg Svc

View full text Add to dashboard Cite

show abstract

“…One of the main drawbacks of intrinsic safety techniques lies in robots slowing down or even stopping altogether in the presence of a person, resulting in a reduction in productivity, which is undesirable for workers. In addition to concerns about productivity, even though intrinsic techniques have been demonstrated to be sufficiently safe [12], users might still experience a sense of risk when entering the area of influence of a robot. This concern arises from the possibility of potential hazards, such as malfunctions in safety mechanisms or risks associated with the robot holding objects.…”

Section: Introductionmentioning

confidence: 99%

Mixed Reality Representation of Hazard Zones while Collaborating with a Robot: Sense of Control over Own Safety

Martin,

Kildal,

Lazkano

2023

Preprint

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Safety is the main concern in human-robot collaboration (HRC) in work environments. Standard safety measures based on reducing robot speed affect productivity of collaboration, and do not inform workers adequately about the state of the robot, leading to stressful situations due to uncertainty. To grant the user control over safety, we investigate using audio, visual and audio-visual mixed reality (MR) displays that inform about the boundaries of zones with different levels of hazard. We describe the design of the hazard displays for scenario of collaboration with a real robot. We then report an experimental user study with 24 users, comparing performance and user experience (UX) obtained with the auditory display, the visual display, and the audio-visual display resulting from combining both. Findings suggest that all modalities are suitable for HRC scenarios, warranting similar performance during collaboration. However, distinct qualitative results were observed between displays, indicating differences in the UX obtained.

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“…To minimize such risks, the most straightforward way is to keep the robots isolated [4]. Numbers of strategy are innovated by integrating different sensors to improve safe human-robot interactions [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

Fine-tuned RetinaNet models for Vision-based Human Presence Detection

Tang,

Ab. Nasir,

P. P. Abdul Majeed

et al. 2022

MEKATRONIKA

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Moving towards Industry 4.0, the idea of human-robot interaction (HRI) and human-robot collaboration (HRC) has been popularized. To introduce more robots into the industries, risk-correlated issues would be always on the hook as robots are not as flexible as human. In fact, although robots can replace human workers in some of the dangerous tasks, still human safety is always the top priority for all industries. The most common way to safeguard the human was to isolate the working space of human workers and robots. To realize the idea of Industry 4.0, it is postulated to have the robots and cobots out of the cage to maximize productivity and efficiency. Hence, studies have been conducted with the attempts to free the robots from the isolated working space while preserve the safety of human operators. The present study seeks to explore the feasibility of transfer learning strategy — fine-tuning to human presence detection tasks as the base of practicing safe HRI. A custom image dataset with 1463 images was collected and separated into train, validation, and test set with a ratio of 70:20:10. Three RetinaNet object detection models with different backbone networks were fine-tuned with the acquired dataset to transfer the knowledge learned from source domain to the target domain, which is the human presence detection tasks. The result has shown that the RetinaNet_ResNet152-V1-FPN has the highest test AP of 74.4% with an inference speed of 13.09 FPS, suggesting that it is the best fine-tuned RetinaNet models. This study has demonstrated the feasibility of using fine-tuning as the strategy to train the object detection models, which can possibly act as the base for improving HRI applications via a deep learning visual-based method. In summary, the research has signified the uses of deep learning models to perform human presence detections and can be further extended for HRI safety applications.

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Dynamic safety system for collaboration of operators and industrial robots

Cited by 22 publications

References 7 publications

Analysis of quality standards for industrial collaborative robots based on user‐centered design framework

Analysis of quality standards for industrial collaborative robots based on user‐centered design framework

Mixed Reality Representation of Hazard Zones while Collaborating with a Robot: Sense of Control over Own Safety

Fine-tuned RetinaNet models for Vision-based Human Presence Detection

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