Seamless human robot collaborative assembly – An automotive case study

Michalos, George; Kousi, Niki; Karagiannis, Panagiotis; Gkournelos, Christos; Dimoulas, Konstantinos; Koukas, Spyridon; Mparis, Konstantinos; Papavasileiou, Apostolis; Makris, Sotiris

doi:10.1016/j.mechatronics.2018.08.006

Cited by 168 publications

(70 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The HG method is indeed a representative function of collaborative robots [30], since it allows even unskilled users to interact with and program the cobot, which can allow some degree of flexibility-even if the robot moves only on predefined directions-without the need for expensive algorithms [35]. It should be noted that the HG method could also be employed with traditional industrial robots, such as a COMAU NJ130 [36]: This allows one to take advantage of the robot's characteristics, such as high speed and power, and increase the system's flexibility. As stated previously, the collaborative mode depends on the considered application.…”

Section: Trends In the Literaturementioning

confidence: 99%

“…To increase safety, productivity, and task performance, researchers will need to improve planners, [39,53], environment and task understanding [28,40,54,55], operator intention understanding [38], and ergonomic cell setups [37,56]. To improve HRI systems, common future work focuses on increasing the robots' and operators' awareness of the task and environment by object recognition [44] and integrating multi-modal sensing in an intuitive manner for the operator [3,32,36].…”

Section: Trends In the Literaturementioning

confidence: 99%

“…Finally, [57] highlights that cobots, presenting different payloads, were preferred with up to 5 kg payload capacity; indeed, they held the largest market size in 2017, and a similar trend is expected to continue from 2018 to 2025. This preference of the market towards lightweight robots, which are safer but do not present the high speed and power typically connected with industrial robots [36,61], restrains the HRC possibilities in the current manufacturing scenario. However, we believe that without proper regulation, the current market will continue to mark a dividing line between heavy-duty tasks and HRC methods.…”

Section: Trend Of the Marketmentioning

confidence: 99%

See 2 more Smart Citations

Human–Robot Collaboration in Manufacturing Applications: A Review

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Section: Trends In the Literaturementioning

confidence: 99%

Section: Trends In the Literaturementioning

confidence: 99%

Section: Trend Of the Marketmentioning

confidence: 99%

See 1 more Smart Citation

Human–Robot Collaboration in Manufacturing Applications: A Review

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In recent years, many approaches have tackled the safety issues to guarantee the human safety assuming that the physical contact is unavoidable. Michalos et al [163,160] summarised the robot safety in three categories, i.e., crash safety (only 'safe'/controlled collisions allowed), active safety (stopping the operation in a controlled way), and adaptive safety (intervening in the operation of and applying corrective actions). Based on assembly process specifications, different control, safety and operator support strategies have to be implemented in order for the human safety and the overall system's productivity to be ensured.…”

Section: Safety Standards and Systemsmentioning

confidence: 99%

Symbiotic human-robot collaborative assembly

et al. 2019

View full text Add to dashboard Cite

In human-robot collaborative assembly, robots are often required to dynamically change their pre-planned tasks to collaborate with human operators in a shared workspace. However, the robots used today are controlled by pre-generated rigid codes that cannot support effective human-robot collaboration. In response to this need, multi-modal yet symbiotic communication and control methods have been a focus in recent years. These methods include voice processing, gesture recognition, haptic interaction, and brainwave perception. Deep learning is used for classification, recognition and context awareness identification. Within this context, this keynote provides an overview of symbiotic human-robot collaborative assembly and highlights future research directions.

show abstract

“…As human workers, robots, the environment, and other components of a particular situation often change rapidly, modeling and planning collaboration tasks for humans and robots in an unstructured environment is a challenging task. Michalos et al [7] presented the implementation of a robotic system for advanced human-robot collaboration assembly and discussed all the technological approaches that have been implemented for facilitating the interaction and support of human operators. Human postures by robots can be broadly perceived as contacting postures (requiring sensors for touching the human body) and non-contacting postures (not requiring sensors that contact the human body).…”

Section: Introductionmentioning

confidence: 99%

Standing-Posture Recognition in Human–Robot Collaboration Based on Deep Learning and the Dempster–Shafer Evidence Theory

Guan

Liu

Cai

et al. 2020

Sensors

View full text Add to dashboard Cite

During human–robot collaborations (HRC), robot systems must accurately perceive the actions and intentions of humans. The present study proposes the classification of standing postures from standing-pressure images, by which a robot system can predict the intended actions of human workers in an HRC environment. To this end, it explores deep learning based on standing-posture recognition and a multi-recognition algorithm fusion method for HRC. To acquire the pressure-distribution data, ten experimental participants stood on a pressure-sensing floor embedded with thin-film pressure sensors. The pressure data of nine standing postures were obtained from each participant. The human standing postures were discriminated by seven classification algorithms. The results of the best three algorithms were fused using the Dempster–Shafer evidence theory to improve the accuracy and robustness. In a cross-validation test, the best method achieved an average accuracy of 99.96%. The convolutional neural network classifier and data-fusion algorithm can feasibly classify the standing postures of human workers.

show abstract

Seamless human robot collaborative assembly – An automotive case study

Cited by 168 publications

References 25 publications

Human–Robot Collaboration in Manufacturing Applications: A Review

Human–Robot Collaboration in Manufacturing Applications: A Review

Symbiotic human-robot collaborative assembly

Standing-Posture Recognition in Human–Robot Collaboration Based on Deep Learning and the Dempster–Shafer Evidence Theory

Contact Info

Product

Resources

About