Development of safety method for a 3-DOF industrial robot based on recurrent neural network

Mahmoud, Khaled Hashim; Sharkawy, Abdel-Nasser; Abdel-Jaber, G. T.

doi:10.1186/s44147-023-00214-8

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…The method was investigated using a 1-DOF manipulator and could be used with any industrial robot. In [10], a recurrent NN (RNN) was used to estimate and detect collisions with a 3-DOF robotic manipulator based only on the signals of joint positions. Thus, their method could be used with any industrial robot.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Pattern Recognition Neural Network for Collision Classification Using Force Sensor Signals

Sharkawy,

Ma’arif,

Furizal

et al. 2023

Robotics

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In this paper, force sensor signals are classified using a pattern recognition neural network (PRNN). The signals are classified to show if there is a collision or not. In our previous work, the joints positions of a 2-DOF robot were used to estimate the external force sensor signal, which was attached at the robot end-effector, and the external joint torques of this robot based on a multilayer feedforward NN (MLFFNN). In the current work, the estimated force sensor signal and the external joints' torques from the previous work are used as the inputs to the proposed designed PRNN, and its output is whether a collision is found or not. The designed PRNN is trained using a scaled conjugate gradient backpropagation algorithm and tested and validated using different data from the training one. The results prove that the PRNN is effective in classifying the force signals. Its effectiveness for classifying the collision cases is 92.8%, and for the non-collisions cases is 99.4%. Therefore, the overall efficiency is 99.2%. The same methodology and work are repeated using a PRNN trained using another algorithm, which is the Levenberg–Marquardt (PRNN-LM). The results using this structure prove that the PRNN-LM is also effective in classifying the force signals, and its overall effectiveness is 99.3%, which is slightly higher than the first PRNN. Finally, a comparison of the effectiveness of the proposed PRNN and PRNN-LM with other previous different classifiers is included. This comparison shows the effectiveness of the proposed PRNN and PRNN-LM.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Pattern Recognition Neural Network for Collision Classification Using Force Sensor Signals

Sharkawy,

Ma’arif,

Furizal

et al. 2023

Robotics

Self Cite

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Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems

Varriale,

Cammarano,

Michelino

et al. 2023

J Intell Manuf

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Scientific research on emerging technologies underscored the advantages of their implementation within production systems, with a particular focus on artificial intelligence (AI). In particular, the integration of AI with other cutting-edge technologies is a relevant topic which can potentially lead to huge impacts in terms of business performance. Yet, literature on the subject, although rich, is still fragmented, limited to specific cases and applications, but lacking in a comprehensive classification framework. Therefore, using a systematic literature review, this study provides an overview of how the combination of AI and other cutting-edge technologies could potentially improve market and organisational performance in business functions and processes. By classifying the literature of case studies and real-world applications into specific taxonomies, the research considers an indicator, the co-occurrence ratio, highlighting the most significant and emerging combinations between AI and other cutting-edge technologies, also specifying the contexts in which they are used. The study shows that AI is strongly interconnected with other cutting-edge technologies, suggesting a research agenda in which the integration of AI with other emerging technologies is promising within specific production systems contexts, providing benefits and opportunities for companies.

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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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Development of safety method for a 3-DOF industrial robot based on recurrent neural network

Cited by 3 publications

References 25 publications

A Comprehensive Pattern Recognition Neural Network for Collision Classification Using Force Sensor Signals

A Comprehensive Pattern Recognition Neural Network for Collision Classification Using Force Sensor Signals

Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems

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

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