“Adult” Robot Enabled Learning Process in High Precision Assembly Automation

Cheng, Hongtai; Chen, Heping

doi:10.1115/1.4026084

Cited by 8 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Very few papers have reported the development of DSS for automating actual manufacturing processes such as machining, assembly and inspections. [10][11][12] For example, Tapoglou et al 17 have developed a cloudbased multi-objective optimisation solution for selecting the machining parameters based on real-time monitoring of the part. Makris et al 18 have developed an algorithm for deciding the sequence of instructions to be shown for assembly support based on the given assembly sequence, semantic classification of the subprocesses and components and the real-time task flow.…”

Section: Related Researchmentioning

confidence: 99%

“…Such systems have been proposed and developed for various applications in manufacturing. Cheng and Chen 10 used the Markov decision process (MDP) that allows one robot to teach a task to another robot in an effort to automate production using a Markov-based decision-making process. However, the Markov assumption means that the next system state depends only on the previous system state without any feed forward to assist in decision-making.…”

Section: Related Researchmentioning

confidence: 99%

See 1 more Smart Citation

Decision support system enabled by depth imaging sensor data for intelligent automation of moving assemblies

Prabhu

Muhandri

Song

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

Automation, driven by informatics, enables manufacturing companies to increase productivity and meet market demands for cost-effective and high-quality products. However, many manufacturing operations across industry verticals continue to be manual even today. One such example is the manual assembly of the final trim and wheels in an automotive production line where there is heavy reliance on human decision-making pertaining to when, where and how to install components on and inside a constantly moving vehicle body. The main aim of this work is to develop a rule-based decision support system that will enable an automation solution to make human-like decisions in moving assembly operations. The wheel loading operation is chosen as a case study and a decision support framework and tool is developed and successfully tested using multiple assembly scenarios generated from experimental data provided by gaming interface sensors installed on the laboratory-based shopfloor. The resulting decision support system has the potential to enable the automation of moving assembly operations in various sectors of the manufacturing industry.

show abstract

Section: Related Researchmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

Decision support system enabled by depth imaging sensor data for intelligent automation of moving assemblies

Prabhu

Muhandri

Song

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

show abstract

“…[1]- [3]). Reinforcement learn- * corresponding author Email addresses: mgaeta@unisa.it (Matteo Gaeta), loia@unisa.it (Vincenzo Loia), semiranda@unisa.it (Sergio Miranda), stomasiello@unisa.it (Stefania Tomasiello ) A C C E P T E D M A N U S C R I P T ing algorithms compute control policies by means of an agent, who can learn directly on the system (on-line control) or from an interaction with a simulator of the system (off-line or batch control).…”

Section: Introductionmentioning

confidence: 95%

Fitted Q-iteration by Functional Networks for control problems

Gaeta

Loia

Miranda

et al. 2016

Applied Mathematical Modelling

View full text Add to dashboard Cite

In this paper a new offline model-free approximate Q-iteration is proposed. Following the idea of Fitted Q-iteration, we use a computational scheme based on Functional Networks, which have been proved to be a powerful alternative to Neural Networks, because they do not require a large number of training samples. We state a condition for the convergence of the proposed technique and we apply it to three classical control problems, namely, a DC motor, a pendulum swing up, a robotic arm. We present a comparative study to show the approximation capabilities of our method with a relatively small number of training samples

show abstract

“…However, the way of the tool center point (TCP) moved from initial pose to contact the hole plate which was not involved. Cheng solved peg-in-hole positioning process by Q-Learning (Cheng and Chen, 2014). After Q-Learning training, the TCP of the robot can move in a ladder-like line from initial pose to the target hole.…”

Section: Introductionmentioning

confidence: 99%

Human skill integrated motion planning of assembly manipulation for 6R industrial robot

Liu

Cong

Dong

et al. 2019

View full text Add to dashboard Cite

Purpose The purpose of this paper is to propose a new method based on three-dimensional (3D) vision technologies and human skill integrated deep learning to solve assembly positioning task such as peg-in-hole. Design/methodology/approach Hybrid camera configuration was used to provide the global and local views. Eye-in-hand mode guided the peg to be in contact with the hole plate using 3D vision in global view. When the peg was in contact with the workpiece surface, eye-to-hand mode provided the local view to accomplish peg-hole positioning based on trained CNN. Findings The results of assembly positioning experiments proved that the proposed method successfully distinguished the target hole from the other same size holes according to the CNN. The robot planned the motion according to the depth images and human skill guide line. The final positioning precision was good enough for the robot to carry out force controlled assembly. Practical implications The developed framework can have an important impact on robotic assembly positioning process, which combine with the existing force-guidance assembly technology as to build a whole set of autonomous assembly technology. Originality/value This paper proposed a new approach to the robotic assembly positioning based on 3D visual technologies and human skill integrated deep learning. Dual cameras swapping mode was used to provide visual feedback for the entire assembly motion planning process. The proposed workpiece positioning method provided an effective disturbance rejection, autonomous motion planning and increased overall performance with depth images feedback. The proposed peg-hole positioning method with human skill integrated provided the capability of target perceptual aliasing avoiding and successive motion decision for the robotic assembly manipulation.

show abstract

“Adult” Robot Enabled Learning Process in High Precision Assembly Automation

Cited by 8 publications

References 20 publications

Decision support system enabled by depth imaging sensor data for intelligent automation of moving assemblies

Decision support system enabled by depth imaging sensor data for intelligent automation of moving assemblies

Fitted Q-iteration by Functional Networks for control problems

Human skill integrated motion planning of assembly manipulation for 6R industrial robot

Contact Info

Product

Resources

About