Symbiotic human-robot collaborative assembly

Wang, Lei; Gao, Robert X.; Váncza, József; Krüger, Jörg; Wang, X.V.; Makris, Sotiris; Chryssolouris, George

doi:10.1016/j.cirp.2019.05.002

Cited by 426 publications

(169 citation statements)

References 228 publications

(272 reference statements)

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“…We can expect that many of these advances can come from other areas of robotics research, such as learning by demonstration through hand-guiding or simulation techniques that make it easy to teach a robot a task, and advances in computer vision and machine learning for object recognition and semantic mapping. Other reviews, such as [8], identify similar trends, namely those of improved modeling and understanding, better task planning, and adaptive learning. It will be very interesting to see how this technology is incorporated into the industrial setting to take full advantage of the mechanics and control of cobots and the HRI methodologies of task collaboration.…”

Section: Trends In the Literaturementioning

confidence: 90%

“…It should be noted that neither this classification nor the terminology used are unique, and others may be found in the literature [8][9][10][11]. To provide definitions and guidelines for the safe and practical use of cobots in industry, several standards have been proposed.…”

mentioning

confidence: 99%

“…Furthermore, they should be equipped with additional features with respect to traditional robots, such as force and torque sensors, force limits, vision systems (cameras), laser systems, anti-collision systems, recognition of voice commands, and/or systems to coordinate the actions of human operators with their motion. For a more complete overview, we refer to [8,13]. Table A1 shows the characteristics of some of the most popular cobots, with a brief overview of some kinematic schemes in Table A2.…”

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confidence: 99%

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Human–Robot Collaboration in Manufacturing Applications: A Review

et al. 2019

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This paper provides an overview of collaborative robotics towards manufacturing applications. Over the last decade, the market has seen the introduction of a new category of robots—collaborative robots (or “cobots”)—designed to physically interact with humans in a shared environment, without the typical barriers or protective cages used in traditional robotics systems. Their potential is undisputed, especially regarding their flexible ability to make simple, quick, and cheap layout changes; however, it is necessary to have adequate knowledge of their correct uses and characteristics to obtain the advantages of this form of robotics, which can be a barrier for industry uptake. The paper starts with an introduction of human–robot collaboration, presenting the related standards and modes of operation. An extensive literature review of works published in this area is undertaken, with particular attention to the main industrial cases of application. The paper concludes with an analysis of the future trends in human–robot collaboration as determined by the authors.

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Section: Trends In the Literaturementioning

confidence: 90%

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confidence: 99%

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Human–Robot Collaboration in Manufacturing Applications: A Review

et al. 2019

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“…In typical scenarios, the target is specified in the first frame only (e.g., defining a rectangle), and it is often meaningful to track the target object in subsequent frames. This tracking can be directly applied in warehouse automation [4], human-robot handovers [5], safety design improvement for human-robot collaboration [4,[6][7][8][9] and human-robot synchronization [10,11]. However, many challenges are encountered in visually tracking an object, which are due to challenging factors such as deformation, illumination variation, scale variation, and partial occlusions.…”

Section: Introductionmentioning

confidence: 99%

Robust Visual Tracking Based on Fusional Multi-Correlation-Filters with a High-Confidence Judgement Mechanism

Wang

Liu

et al. 2020

Applied Sciences

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Visual object trackers based on correlation filters have recently demonstrated substantial robustness to challenging conditions with variations in illumination and motion blur. Nonetheless, the models depend strongly on the spatial layout and are highly sensitive to deformation, scale, and occlusion. As presented and discussed in this paper, the colour attributes are combined due to their complementary characteristics to handle variations in shape well. In addition, a novel approach for robust scale estimation is proposed for mitigatinge the problems caused by fast motion and scale variations. Moreover, feedback from high-confidence tracking results was also utilized to prevent model corruption. The evaluation results for our tracker demonstrate that it performed outstandingly in terms of both precision and accuracy with enhancements of approximately 25% and 49%, respectively, in authoritative benchmarks compared to those for other popular correlation- filter-based trackers. Finally, the proposed tracker has demonstrated strong robustness, which has enabled online object tracking under various scenarios at a real-time frame rate of approximately 65 frames per second (FPS).

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“…Settling this aim for the industrial robotics sector would require freeing robots from their current work cells, closer to operators, compromising human safety [1,2]. In the interest of overcoming those safety issues, over the last few years, collaborative robots or cobots have emerged [3][4][5]. These robots are specifically designed for direct interaction with a human within a defined collaborative workspace [6].…”

Section: Introductionmentioning

confidence: 99%

On Inferring Intentions in Shared Tasks for Industrial Collaborative Robots

2019

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Inferring human operators’ actions in shared collaborative tasks plays a crucial role in enhancing the cognitive capabilities of industrial robots. In all these incipient collaborative robotic applications, humans and robots not only should share space, but also forces and the execution of a task. In this article, we present a robotic system that is able to identify different human’s intentions and to adapt its behavior consequently, only employing force data. In order to accomplish this aim, three major contributions are presented: (a) a force based operator’s intention recognition system based on data from only two users; (b) a force based dataset of physical human–robot interaction; and (c) validation of the whole system with 15 people in a scenario inspired by a realistic industrial application. This work is an important step towards a more natural and user-friendly manner of physical human–robot interaction in scenarios where humans and robots collaborate in the accomplishment of a task.

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Symbiotic human-robot collaborative assembly

Cited by 426 publications

References 228 publications

Human–Robot Collaboration in Manufacturing Applications: A Review

Human–Robot Collaboration in Manufacturing Applications: A Review

Robust Visual Tracking Based on Fusional Multi-Correlation-Filters with a High-Confidence Judgement Mechanism

On Inferring Intentions in Shared Tasks for Industrial Collaborative Robots

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