Predicting and preventing mistakes in human-robot collaborative assembly

Antonelli, Dario; Stadnicka, Dorota

doi:10.1016/j.ifacol.2019.11.204

Cited by 19 publications

(3 citation statements)

References 15 publications

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“…Consistent with the work from Marvel et al, 12 the proposed methodology starts with the analysis of the collaborative process through the application of the Hierarchical Task Analysis. 33,34 The literature suggests a task-based approach for safety risk assessment 17,24,35 that aids the user in carefully figuring out the dynamics of the actions.…”

Section: Stage 1 -Task Branchingmentioning

confidence: 99%

Exploring a cobot risk assessment approach combining FMEA and PRAT

Murino

Nardo

Pollastro

et al. 2022

Quality & Reliability Eng

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Collaborative robots are an emerging technology falling within the scope of Industry 4.0 and based on the concept of Human-Robot Collaboration (HRC). Unlike traditional industrial robots, collaborative robots are used in shared workspaces with no safety fences. Hence, prospective hazardous contacts need to be avoided or mitigated through a risk assessment. The normative standards such as ISO TS 15066 suggest a list of common hazards, but do not guide the robot system user through the risk assessment process. To address this shortcoming, this paper proposes a practical eight steps risk assessment approach, resulting in a risk priority list. In order to provide an accurate, practical, quantitative and supportive tool for HRC environments, the Failure Mode and Effects Analysis (FMEA) and the Proportional Risk Assessment technique (PRAT) techniques are proposed for risk assessment. The two techniques mentioned above are combined in the suggested new methodology, highlighting both their benefits and disadvantages. The proposed methodology is applied with positive results to a collaborative brick-lifter case study.

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Section: Stage 1 -Task Branchingmentioning

confidence: 99%

Exploring a cobot risk assessment approach combining FMEA and PRAT

Murino

Nardo

Pollastro

et al. 2022

Quality & Reliability Eng

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“…This indicates that there is a clear attempt from the community to improve and simplify collaborative technologies to obtain broader industry adoption. Examples of HRC system using supportive roles between humans and robots include [7] and [8]. The second dominant interaction level was found to be the sequential systems which represent 26% of the identified HRC systems (Fig.…”

Section: Interaction Levelsmentioning

confidence: 99%

Human-Robot Collaboration Systems: Components and Applications

Segura¹,

Calleros²,

Ramírez-Serrano³

2020

International Conference of Control, Dynamic Systems, and Robotics

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Collaborative robots (cobots) have emerged as a technological solution for enhanced manipulation of objects while allowing safe interaction with a human counterpart. Although substantial developments in Human-Robot Collaboration (HRC) systems have taken place in the last decade, no conceptual framework of their composition is available. The literature provides an unclear process of how to integrate human-robot interaction levels and their roles with safety and communication technologies into HRC systems. To design an ergonomic HRC system (in its physical and cognitive dimensions) a clear identification and categorization of its components is required. This paper presents a literature review analysis that identifies the tendencies of HRC in the manufacturing sector. An initial distinction between application by type of industry and task is carried out. Afterward, interaction levels in HRC systems are examined, both individually and collectively, depending on the application area. Work roles of humans and robots, safety settings, and communication interfaces are also analyzed as part of the interaction levels in the primary composition of HRC systems. Their presence and distribution along 50 selected cases are also explored. The analyzed data, results, and observations presented in this paper demonstrate clear tendencies for specific components that were identified as necessary for improving future designs of human-centered HRC systems.

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“…Human-robot collaboration (HRC) combines the precision of robots with the skills of human workers to make manufacturing more flexible and improve product quality and productivity. One of the important issues of HRC is to ensure the safety of workers in collaboration (Pablo et al, 2022;Antonelli & Stadnicka, 2019). In general, HRC systems ensure the safety of workers by creating a collision-free trajectory based on distance between the human and the robot using a visual sensor (Halme et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Emotion Recognition Using Deep Learning Model for Workers Safet

2024

nano-ntp

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Recently, industrial robots and collaborative robots are widely used in industrial sites with the introduction of smart factory. In a human-robot collaboration environment, it is important to ensure the safety of workers above all. This study suggested an EEG-based deep learning modelbased worker safety management system that guards employees by identifying their feelings when they perceive risk. We evaluated and examined the performance of the suggested CNN, DNN, LSTM, and CNN-LSTM models in order to determine which deep learning model would work best for EEG-based emotion identification. With 71.3% accuracy while utilizing the SEED dataset as input information, the CNN-LSTM model demonstrated good performance; with 74.4% accuracy, the CNN model demonstrated good performance when using the real gathered data set. The proposed deep learning model has a small number of parameters, a small size, and fast processing time, which is advantages for real-time application.

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Predicting and preventing mistakes in human-robot collaborative assembly

Cited by 19 publications

References 15 publications

Exploring a cobot risk assessment approach combining FMEA and PRAT

Exploring a cobot risk assessment approach combining FMEA and PRAT

Human-Robot Collaboration Systems: Components and Applications

EEG-Based Emotion Recognition Using Deep Learning Model for Workers Safet

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