Towards easy setup of robotic assembly tasks

Sloth, Christoffer; Kramberger, Aljaž; Iturrate, Iñigo

doi:10.1080/01691864.2019.1704869

Cited by 11 publications

(22 citation statements)

References 10 publications

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“…For 3C product assembly applications, no matter which LfD method is selected to learn the assembly skill, it is more efficient to carry out the indirect demonstration. It is because human operators can naturally perform bimanual assembly indirect demonstration with their experience [3], [14]. Accordingly, what matters in 3C product assembly demonstration data is the absolute error since what we obtained are multiple motion trajectories of 3C parts without the robots' actively tracking.…”

Section: ) Trajectory Performance Evaluationmentioning

confidence: 85%

“…Figure 7 shows the placement of linear and circular motion agents in the workspace. (3). In order to express the performance of each device in twelve test blocks intuitively, Figures 9 and 10 exhibit the numerical value in the form of histograms.…”

Section: ) Trajectory Performance Evaluationmentioning

confidence: 99%

“…But one suffers a crucial issue when applying robots to 3C product assembly lines -the tedious robot re-programming [2]. To address these problems and make robotic assembly systems applicable to 3C product assembly, one approach is to make the robot programming of the 3C product assembly tasks intuitive such that robot programming can be accomplished by workers without robot programming knowledge [3]. Learning from demonstration [4] is a feasible solution.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the practical factory environment, the optical MoCap has better robustness than other motion capture techniques, e.g. acoustic and electromagnetic motion capture systems Compared with teleoperation [16] and kinesthetic teaching [3] based demonstration technique, optical MoCaps basically do not affect the activities of workers. With the infrared cameras around the workspace of 3C product assembly, an optical MoCap can obtain demonstration data consisting of poses of fingertips, finger joints, palm, and so forth by sensing the position of markers on the worker's hands.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Performance Evaluation of Optical Motion Capture Sensors for Assembly Motion Capturing

Cao

Yang

et al. 2021

IEEE Access

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The optical motion capture (MoCap) sensor provides an effective way to capture human motions and transform them into valuable data that can be applied to certain tasks, e.g. robot learning from demonstration (LfD). In spite of the wide utilization of optical MoCaps in LfD studies, there are few works that explore their potentiality in small parts robotic assembly. Robot manipulation skill learning from demonstration has gained the attention of researchers recently and robotic 3C (Computer, Communication, and Consumer Electronics) product assembly turns out to be a promising application thanks to the increasing consumption of 3C products. To further explore the potential of optical MoCaps in robotic 3C product assembly. This work proposes a performance evaluation protocol that takes the characters of both optical MoCaps and 3C product assembly operations into account. The proposed evaluation protocol includes static and trajectory evaluations. The former refers to the widely used evaluation indicators such as precision and accuracy. Meanwhile, the trajectory evaluation takes contour error as an error metric. Three popular optical MoCaps are studied in the experiment. Experiment results show that the static performance of all of the three optical MoCaps can meet the requirements of the 3C product assembly task. What's more, Prime X41 possesses the best trajectory performance. This work sheds light on the wider usage of optical MoCaps in manufacturing industries.

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Section: ) Trajectory Performance Evaluationmentioning

confidence: 85%

Section: ) Trajectory Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance Evaluation of Optical Motion Capture Sensors for Assembly Motion Capturing

Cao

Yang

et al. 2021

IEEE Access

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show abstract

“…Current industrial robots are notoriously difficult to program, leading to high change-over time and expert labor consumption. To make robotic assembly applicable to lowvolume high-mix production, one approach is to make the programming of assembly tasks so intuitive that can be accomplished by workers without traditional robot programming skills [26,29]. Learning from demonstration (LfD) [23], or programming by demonstration [9], is a paradigm that aims to transfer human's skills to robots by human demonstration instead of the unintuitive and tedious robot programming by expert robot users [10,30].…”

Section: Introductionmentioning

confidence: 99%

A robot learning from demonstration framework for skillful small parts assembly

Yang

Lou

2022

Int J Adv Manuf Technol

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Increasing demand for higher production flexibility and smaller production batch size pushes the development of manufacturing expertise towards robotic solutions with fast setup and reprogram capability. Aiming to facilitate assembly lines with robots, the learning from demonstration (LfD) paradigm has attracted attention. A robot LfD framework designed for skillful small parts assembly applications is developed, which takes position, orientation and wrench demonstration data into consideration while utilizes impedance control to deal with the motion error. In view of constraints in industrial assembly applications, we propose a robot LfD framework where policy learning is carried out with separated assembly demonstration data to avoid potential under-fitting problem. With the proposed assembly policies, reference orientation and wrench trajectories are generated as well as coupled with the position data to boost their generalization and robust performance. Effectiveness of the proposed LfD framework is validated by a printed circuit board assembly experiment with a 7-DOF torque-controlled robot.

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Design, simulation and robotic assembly of reversible timber structures

et al. 2021

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Wood is more and more seen as a sustainable solution to offset carbon emissions from constructions. In response and in parallel to this, the research in robotic timber construction is evolving rapidly, pushed by Industry 4.0 technologies and the integration of digital and physical robotic assets. This paper presents an approach for the design and assembly automation of layered timber structures, with the use of a flexible cell based on collaborative robots. Advanced assembly procedures and digital design of non-standard timber structures are here established and integrated. The automation process is here enhanced by the (1) use of feedback systems based on the location and force signals, (2) the introduction of a flexible robot setup with automatic screwing, and (3) human-collaboration to provide immediate assistance to the robot in the case the signals do not match the defined assembly conditions. The paper discusses the development and use of a Cyber-Physical System to govern the entire construction process, including reflections on the integrated approach to the design, modelling and simulation of the process.

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Towards easy setup of robotic assembly tasks

Cited by 11 publications

References 10 publications

Performance Evaluation of Optical Motion Capture Sensors for Assembly Motion Capturing

Performance Evaluation of Optical Motion Capture Sensors for Assembly Motion Capturing

A robot learning from demonstration framework for skillful small parts assembly

Design, simulation and robotic assembly of reversible timber structures

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