Integrated Sensors System for Human Safety during Cooperating with Industrial Robots for Handing-over and Assembling Tasks

Bdiwi, Mohamad

doi:10.1016/j.procir.2014.10.099

Cited by 27 publications

(8 citation statements)

References 13 publications

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“…The use of sensors has become crucial in robotics to let the technology gather data to perform its tasks and interact in a safe way with its environment [59][60][61]. However, this leads to increasingly problematic privacy issues given the constant interaction of robots with humans [62].…”

Section: Privacy and Surveillance As Trade-offsmentioning

confidence: 99%

Workplace 4.0: Exploring the Implications of Technology Adoption in Digital Manufacturing on a Sustainable Workforce

et al. 2022

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As part of the Industry 4.0 movement, the introduction of digital manufacturing technologies (DMTs) poses various concerns, particularly the impact of technology adoption on the workforce. In consideration of adoption challenges and implications, various studies explore the topic from the perspective of safety, socio-economic impact, technical readiness, and risk assessment. This paper presents mixed methods research to explore the challenges and acceptance factors of the adoption of human-robot collaboration (HRC) applications and other digital manufacturing technologies from the perspective of different stakeholders: from manufacturing employees at all levels to legal experts to consultants to ethicists. We found that some of the prominent challenges and tensions inherent in technology adoption are job displacement, employee’s acceptance, trust, and privacy. This paper argues that it is crucial to understand the wider human factors implications to better strategize technology adoption; therefore, it recommends interventions targeted at individual employees and at the organisational level. This paper contributes to the roadmap of responsible DMT and HRC implementation to encourage a sustainable workforce in digital manufacturing.

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Section: Privacy and Surveillance As Trade-offsmentioning

confidence: 99%

Workplace 4.0: Exploring the Implications of Technology Adoption in Digital Manufacturing on a Sustainable Workforce

et al. 2022

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“…Their experiments were conducted on an ABB IRB140 industrial robot. Bdiwi [8] developed a robotic system involving several types of sensors to keep humans safe while interacting with robots. The sensors used were vision, sensitive skin, and force sensors.…”

Section: Related Workmentioning

confidence: 99%

Neural Network-Based Classifier for Collision Classification and Identification for a 3-DOF Industrial Robot

Mahmoud,

Abdel-Jaber,

Sharkawy

2024

Automation

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In this paper, the aim is to classify torque signals that are received from a 3-DOF manipulator using a pattern recognition neural network (PR-NN). The output signals of the proposed PR-NN classifier model are classified into four indicators. The first predicts that no collisions occur. The other three indicators predict collisions on the three links of the manipulator. The input data to train the PR-NN model are the values of torque exerted by the joints. The output of the model predicts and identifies the link on which the collision occurs. In our previous work, the position data for a 3-DOF robot were used to estimate the external collision torques exerted by the joints when applying collisions on each link, based on a recurrent neural network (RNN). The estimated external torques were used to design the current PR-NN model. In this work, the PR-NN model, while training, could successfully classify 56,592 samples out of 56,619 samples. Thus, the model achieved overall effectiveness (accuracy) in classifying collisions on the robot of 99.95%, which is almost 100%. The sensitivity of the model in detecting collisions on the links “Link 1, Link 2, and Link 3” was 97.9%, 99.7%, and 99.9%, respectively. The overall effectiveness of the trained model is presented and compared with other previous entries from the literature.

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“…The force sensor will help the robot to react to the motion of the human hand during the assembling task. Also, a skin sensor will prevent any collision between the human and the robot arm [8]. The essential health and safety requirement related to ergonomic principles (EHSR 1.1.6 Ergonomics) should be adapted to consider the human robot coexistence in a shared space (see Table 2).…”

Section: Implications Of Industry 40 Technologies Embedded On the Mamentioning

confidence: 99%

Risk Assessment on Machines With Ce Marking and With Embedded Industry 4.0 Enabling Technologies

2020

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The Machinery Directive 2006/42/EC introduced the obligation for the manufacturer to carry out risk assessment on machines in the design and construction phase according to a structured and iterative process as described in the ISO 12100 standard that also provides the aspects to be taken into account to identify all hazards and the associated risk. However, the rapid technological development that is characterizing the fourth industrial revolution is leading to equipping the machines with increasingly sophisticated sensors and technologies and this implies new assessments on the safety aspects. In particular, new human-machine interaction, smart machines with learning ability, machines equipped with sensors that report in real time their operating and health status thanks to the connection made possible by the Internet of Things, the potentiality of augmented reality, are all aspects that now must be considered. Therefore, the aim of this paper is to show the risk assessment process by proposing the well-suited techniques used in each of its phases and to highlight the new safety aspects to be taken into account when the machines with CE marking are equipped with the Industry 4.0 enabling technologies. Future developments in standardization and research in this direction will be tracked.

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Integrated Sensors System for Human Safety during Cooperating with Industrial Robots for Handing-over and Assembling Tasks

Cited by 27 publications

References 13 publications

Workplace 4.0: Exploring the Implications of Technology Adoption in Digital Manufacturing on a Sustainable Workforce

Workplace 4.0: Exploring the Implications of Technology Adoption in Digital Manufacturing on a Sustainable Workforce

Neural Network-Based Classifier for Collision Classification and Identification for a 3-DOF Industrial Robot

Risk Assessment on Machines With Ce Marking and With Embedded Industry 4.0 Enabling Technologies

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