Dual-Arm Peg-in-Hole Assembly Using DNN with Double Force/Torque Sensor

Ortega-Aranda, David; Jimenez-Vielma, Julio Fernando; Saha, Baidya Nath; López-Juárez, Ismael

doi:10.3390/app11156970

Cited by 7 publications

(4 citation statements)

References 32 publications

(49 reference statements)

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“…As a result, they can assist in tasks that require human dexterity and decision making while reducing the risk of workplace accidents. From manufacturing assembly lines and assembly techniques [6] to healthcare settings, cobots are being adopted across multiple industries to enhance human capabilities.…”

Section: Collaborative Robotsmentioning

confidence: 99%

Special Issue on Trends and Challenges in Robotic Applications

Gracia

Pérez-Vidal

2023

Applied Sciences

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Section: Collaborative Robotsmentioning

confidence: 99%

Special Issue on Trends and Challenges in Robotic Applications

Gracia

Pérez-Vidal

2023

Applied Sciences

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“…Moreover, it is necessary to use contactless demonstration, and a robot gripper as an end effector provides more flexible automation tasks than lead through robot programming. Compared with the single robot arm, the dual arm [11,12] is more suitable to simulate the human behavior of assembly tasks in manufacturing.…”

Section: Introduction mentioning

confidence: 99%

Strategy with machine learning models for precise assembly using programming by demonstration

Bai

Hsieh

2023

Preprint

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Programming by Demonstration(PBD) applies in the industry as a method of human-robot collaboration for assembly, such as placing, inserting, and screwing; however, PBD has not received much attention in commercial electronic product assembly. There is little work on integrating a strategy to optimize trajectory planning, which is an initial step of PBD. Most recent related works improving PBD performance focus on taking care of numerous demonstration samples from motion-sensing devices under time consumption, distance, and other related criteria. However, it is necessary to integrate a strategy to optimize the performance of precise assembly tasks. We proposed a framework with two custom algorithms to pre-process and classify contactless demonstration performance in this research work. It verified that these algorithms could more effectively generate optimal motion paths in criteria of distance, smoothness, and trajectory variance than canonical methods. Machine learning methods, including the Convolutional neural network(CNN), Artificial Neural Network(ANN), and Support Vector Machine(SVM), are feasible to predict the further performance that the best motion path with accuracy ranging from 80% to 85% accuracy. Also, CNN performs better than ANN and SVM. Among CNN methods, the DarkNet yields the highest accuracy rate of 85%. Future work includes the hybrid CNN/ANN algorithm, which may yield higher accuracy in prediction. Also, the proposed algorithms may apply to robots with dual assembly arms which mimic human arms.

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“…In the rear axle assembly production line, due to the vibration of the tightening gun, the wear of the fixture and the plastic deformation of the workpiece material, the output torque of the tightening gun reaches the assembly threshold, which affects the assembly quality of the rear axle. Therefore, predicting the torque value in the rear axle assembly process can monitor the assembly process in real-time before the quality problem occurs to ensure the assembly quality of the rear axle [4,5]. Many researchers have tried to establish bolt assembly torque prediction methods, which can be roughly divided into two categories: mechanism model method [ 6 ] and data-driven method [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Multi-step prediction of automobile rear axle assembly torque based on adaptive time series data decomposition hybrid deep learning model

Xu,

Zhang,

Gao

et al. 2023

Preprint

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The prediction of assembly torque of automobile rear axles is crucial for the timely detection of assembly abnormalities and early feedback control to reduce the number of production line shutdowns. However, due to the complex assembly conditions, the assembly torque change has non-stationary and abnormal mutation characteristics, and the conventional prediction model is difficult to accurately predict. Therefore, this article proposes an adaptive time series data decomposition hybrid deep learning assembly torque multi-step prediction framework based on the GWO-VMD-CNN-BiLSTM-Attention model. Firstly, the preprocessed original assembly torque sequence will be decomposed into multiple subsequences by an improved adaptive variational mode decomposition method based on grey wolf optimization (GWO). Then, the subsequence is subjected to 1D-CNN convolution to obtain local features. The BiLSTM layer is used to predict the feature data output by the CNN layer, and the Attention mechanism is added to weight the output data of the BiLSTM layer to extract the torque change characteristics in different time steps. Finally, iterative multi-step prediction is performed on each subsequence of assembly torque. Six-steps prediction experiments were carried out on the actual production data of the actual automobile rear axle assembly process. The highest accuracy of the proposed prediction method reaches 98.5%, 97.8%, 95.4%, 91.3%, 89.7%, and 86.5%, respectively. The experimental results show that the proposed model has higher prediction accuracy than other benchmark models. The model is coupled with the digital twin system and deployed, which realizes the real-time prediction of assembly torque and has a high industrial application value.

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Dual-Arm Peg-in-Hole Assembly Using DNN with Double Force/Torque Sensor

Cited by 7 publications

References 32 publications

Special Issue on Trends and Challenges in Robotic Applications

Special Issue on Trends and Challenges in Robotic Applications

Strategy with machine learning models for precise assembly using programming by demonstration

Multi-step prediction of automobile rear axle assembly torque based on adaptive time series data decomposition hybrid deep learning model

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