Safety assurance mechanisms of collaborative robotic systems in manufacturing

Zhu, Bi; Luo, Chaomin; Miao, Zhonghua; Zhang, Bing; Zhang, W. J.; Wang, Lihui

doi:10.1016/j.rcim.2020.102022

Cited by 128 publications

(49 citation statements)

References 61 publications

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“…However, in most cases, this ideal configuration cannot be applied. For these cases, as in [ 1 ], we chose to make a placement adapted to our laboratory, with an irregular distribution of the cameras, like the kind we can find in a common laboratory session. Despite this, in our systematic evaluation of the entire workspace, a lower value (0.6239 mm) of position error was obtained, but not considering the samples that could not be reconstructed by the manufacturer’s software.…”

Section: Discussionmentioning

confidence: 99%

“…Human–robot collaborative shared environments are a topic of growing interest in industry, because of the cost reduction and productivity improvement that come with having robots and humans sharing the same workspace. In this type of production cell, it is mandatory to track the human operator’s behaviour in order to guarantee their safety [ 1 , 2 , 3 ]. Depending on the task specifications, different sensors can be used to that end, e.g., to measure contact forces, body segment position, or velocity [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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Metrological Evaluation of Human–Robot Collaborative Environments Based on Optical Motion Capture Systems

Gonzalez

Álvarez

López

et al. 2021

Sensors

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In the context of human–robot collaborative shared environments, there has been an increase in the use of optical motion capture (OMC) systems for human motion tracking. The accuracy and precision of OMC technology need to be assessed in order to ensure safe human–robot interactions, but the accuracy specifications provided by manufacturers are easily influenced by various factors affecting the measurements. This article describes a new methodology for the metrological evaluation of a human–robot collaborative environment based on optical motion capture (OMC) systems. Inspired by the ASTM E3064 test guide, and taking advantage of an existing industrial robot in the production cell, the system is evaluated for mean error, error spread, and repeatability. A detailed statistical study of the error distribution across the capture area is carried out, supported by a Mann–Whitney U-test for median comparisons. Based on the results, optimal capture areas for the use of the capture system are suggested. The results of the proposed method show that the metrological characteristics obtained are compatible and comparable in quality to other methods that do not require the intervention of an industrial robot.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Metrological Evaluation of Human–Robot Collaborative Environments Based on Optical Motion Capture Systems

Gonzalez

Álvarez

López

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The first robot to be introduced in human life was a robot arm manipulator used in industries to replace human labor in a dull, dangerous, and dirty working environment [1,2,3,4]. This type of robot has been so wellrecognized and well-researched that it creates a perfect worker for industry revolution 4.0 [5].…”

Section: Introductionmentioning

confidence: 99%

“…The use of many simple robots is more profitable than using a single robot with high technical specifications. Although most multi-robots found today are mobile robots [7,8,9], this idea can also be applied to robot arms [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

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Motion Control Analysis of Two Collaborative Arm Robots in Fruit Packaging System

Dewi

Anggraini

Risma

et al. 2021

Sinergi

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As robots' use increases in every sector of human life, the demand for cheap and efficient robots has also enlarged. The use of two or more simple robot is preferable to the use of one sophisticated robot. The agriculture industry can benefit from installing a robot, from seeding to the packaging of the product. A precise analysis is required for the installation of two collaborative robots. This paper discusses the motion control analysis of two collaborative arms robots in the fruit packaging system. The study begins with the relative motion analysis between two robots, starting with kinematics modeling, image processing for object detection, and the Fuzzy Logic Controller's design to show the relationship between the robot inputs and outputs. The analysis is carried out using SCILAB, open-source software for numerical computing engineering. This paper is intended as the initial analysis of the feasibility of the real experimental system.

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Protective and Collision‐Sensitive Gel‐Skin: Visco‐Elastomeric Polyvinyl Chloride Gel Rapidly Detects Robot Collision by Breaking Electrical Charge Accumulation Stability

Hwang,

Nam,

Kim

et al. 2024

Advanced Intelligent Systems

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Human–robot collaboration (HRC) is effective to improve productivity in industrial fields, based on the robot's fast and precise work and the human's flexible skill. To facilitate the HRC system, the first priority is to ensure safety in the event of accidents, such as collisions between robots and humans. Therefore, a protective and collision‐sensitive robot skin, named Gel‐Skin is proposed to guarantee the safety in HRC. The Gel‐Skin is composed of polyvinyl chloride (PVC) gel, which is a functional material with piezoresistive characteristics and impact absorption capability. In particular, the PVC gel has a distinctive piezoresistive property that the relation between mechanical pressure and electrical resistance is tunable depending on an applied voltage. When a voltage is applied to the PVC gel, the electrical charges are accumulated around the anode and it shows increased piezoresistive sensitivity. In this study, it is verified for the PVC gel to exhibit the 4.78 times higher sensitivity by simply applying a voltage. This novel physical phenomenon enables the Gel‐Skin to detect the collision rapidly. Finally, the Gel‐Skin is applicated to a real robot system and it is verified that the Gel‐Skin can detect a collision and absorb impact to ensure safety.

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Safety assurance mechanisms of collaborative robotic systems in manufacturing

Cited by 128 publications

References 61 publications

Metrological Evaluation of Human–Robot Collaborative Environments Based on Optical Motion Capture Systems

Metrological Evaluation of Human–Robot Collaborative Environments Based on Optical Motion Capture Systems

Motion Control Analysis of Two Collaborative Arm Robots in Fruit Packaging System

Protective and Collision‐Sensitive Gel‐Skin: Visco‐Elastomeric Polyvinyl Chloride Gel Rapidly Detects Robot Collision by Breaking Electrical Charge Accumulation Stability

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