Collaborative manufacturing with physical human–robot interaction

Reconfigurable manufacturing systems (RMSs), which possess the advantages of both dedicated serial lines and flexible manufacturing systems, were introduced in the mid-1990s to address the challenges initiated by globalization. The principal goal of an RMS is to enhance the responsiveness of manufacturing systems to unforeseen changes in product demand. RMSs are costeffective because they boost productivity, and increase the lifetime of the manufacturing system. Because of the many streams in which a product may be produced on an RMS, maintaining product precision in an RMS is a challenge. But the experience with RMS in the last 20 years indicates that product quality can be definitely maintained by inserting in-line inspection stations. In this paper, we formulate the design and operational principles for RMSs, and provide a state-of-the-art review of the design and operations methodologies of RMSs according to these principles. Finally, we propose future research directions, and deliberate on how recent intelligent manufacturing technologies may advance the design and operations of RMSs.

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“…The recent development of collaborative robot systems [109][110][111] may offer a way to improve the efficiency and adaptiveness of RASs.…”

Section: From Mass Customization To Mass Individualizationmentioning

confidence: 99%

Reconfigurable manufacturing systems: Principles, design, and future trends

2017

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“…[6] • Speed and separation monitoring -Developing a solution for human-robot collaboration in the automotive industry (PSA) for the assembly process. [7] • Sensors -implementation and integration of different types of sensors to establish required safety for humanrobot collaborations [8] ISO 15066 standard gives advice for human-robot co-operation safety issues. These are recommended for the development of robot cell solutions.…”

Section: Background and Current Situationmentioning

confidence: 99%

Collaborative Work Between Human And Industrial Robot In Manufacturing By Advanced Safety Monitoring System

Kuts¹,

Sarkans²,

Otto³

et al. 2017

DAAAM Proceedings

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Collaborative work between human and industrial robot in different areas is developing rapidly. High payload industrial robots that can harm humans execute their tasks in separated protected areas and are regulated by safety standards. The main aim of this study was to design experiments, analyze, and model the collaborative work and safety monitoring systems by using small industrial robots with inbuilt power and force limiting. Different methods of collaboration are standardized by the ISO/TS 15066:2016. Human co-work with robots gives us a variety of options that can be used in manufacturing and other related areas. The advanced multi-level monitoring system of co-work processes can reach the highest level of the human safety with minimum robot downtime. Several methods were analyzed to find the optimal solution for the online monitoring processes. As a result, a safety multi-level monitoring system was designed, tested in simulation software with real-room conditions with an industrial robot. Safety multi-level system, as a part of the control and monitoring online system consist of different types of sensors and a microcontroller that can be attached to the Cyber Physical Production System (CPPS). Those control and monitoring methods and processes were implemented in experimental setup in Robot Operating System (ROS).

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“…Another consideration on using robots and machinery together with people in productive environments has to do with protecting the physical integrity of operators, for which (Cherubini, Passama, Crosnier, Lasnier, & Fraisse, 2016), recommend using assisted tools with computer vision to better control the distance between elements. However, not only should computer vision be used as a simple distance sensor, as mentioned by (Schroter, Kuhlang, Finsterbusch, Kuhrke, & Veri, 2016) computer vision is a key element in determining large movement routes of actuators, such as robotic arms, tools that prevent collisions with people or other equipment.…”

Section: Interactionmentioning

confidence: 99%

Automatic Learning Improves Human-Robot Interaction in Productive Environments

Hernandez

Marin

García-Rodríguez

et al. 2017

International Journal of Computer Vision and Image Processing

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In the creation of new industries, products and services --all of which are advances of the Fourth Industrial Revolution --the human-robot interaction that includes automatic learning and computer vision are elements to consider since they promote collaborative environments between people and robots. The use of machine learning and computer vision provides the tools needed to increase productivity and minimizes delivery reaction times by assisting in the optimization of complex production planning processes. This review of the state of the art presents the main trends that seek to improve human-robot interaction in productive environments, and identifies challenges in research as well as in industrial -technological development in this topic. In addition, this review offers a proposal on the needs of use of artificial intelligence in all processes of industry 4.0 as a crucial linking element among humans, robots, intelligent and traditional machines; as well as a mechanism for quality control and occupational safety.

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Collaborative manufacturing with physical human–robot interaction

Cited by 470 publications

References 35 publications

Reconfigurable manufacturing systems: Principles, design, and future trends

Reconfigurable manufacturing systems: Principles, design, and future trends

Collaborative Work Between Human And Industrial Robot In Manufacturing By Advanced Safety Monitoring System

Automatic Learning Improves Human-Robot Interaction in Productive Environments

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