Grasping detection of dual manipulators based on Markov decision process with neural network

Yun, Juntong; Jiang, Du; Huang, Li; Tao, Bo; Liao, Shangchun; Liu, Ying; Liu, Xin; Li, Gongfa; Chen, Disi; Chen, Baojia

doi:10.1016/j.neunet.2023.09.016

Cited by 8 publications

(3 citation statements)

References 46 publications

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“…Feature learning is the dimensional transformation of data in the original space so that it contains only the most essential information and the type to which it belongs [20,21]. DeepCluster [22] jointly learned the neural network parameters and the clustering assignment of the resulting features.…”

Section: Clustering For Unsupervised Feature Learningmentioning

confidence: 99%

Unsupervised method for detecting surface defects in steel based on joint optimization of pseudo-labeling and clustering

Bai,

Li,

Jiang

et al. 2024

Meas. Sci. Technol.

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Advances in the field of measurement science and technology have improved the detection of defects in industrial production. One of the key challenges in steel plate surface defect detection is the need to quickly detect a small number of defects in an overwhelmingly defect-free sample. Unlike supervised learning, which relies heavily on precise sample labeling, unsupervised learning leverages its inherent learning capabilities for detection. This paper introduces an innovative method for smart steel diagnosis, integrating joint optimization of feature extraction and clustering. The proposed approach merges mini-batch K-Means clustering with a feature extraction network to acquire pseudo-label information for current images. It employs a multi-view transformation strategy, enabling classification through the optimized feedback from pseudo-labels. This method allows the network to self-optimize the distinction of image features through backpropagation. The method exhibits a mere 4% classification failure rate for steel surface images. This significant reduction in additional data processing requirements enhances the inspection system's efficiency and accuracy. Furthermore, the versatility of this method extends beyond steel defect diagnosis. It holds potential for application in various engineering domains, particularly in scenarios characterized by data imbalance.

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Section: Clustering For Unsupervised Feature Learningmentioning

confidence: 99%

Unsupervised method for detecting surface defects in steel based on joint optimization of pseudo-labeling and clustering

Bai,

Li,

Jiang

et al. 2024

Meas. Sci. Technol.

Self Cite

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show abstract

“…In this work, the fitting ability of the PADM was evaluated by the PV and RMS of the residual surface shape ΔF(x, y). [77][78][79][80] ΔΦ(x, y) = Φ(x, y) − Ψ(x, y), (7) F I G U R E 9 FEA simulation for hexagonal (A) and square configuration (B).…”

Section: Algorithm For Voltage Calculationmentioning

confidence: 99%

Design of novel deformable mirror with large displacement

Li,

Tian,

Wang

et al. 2023

Concurrency and Computation

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SummaryA stabilized zoom system with deformable mirrors (DMs) was designed for continuous optical zoom. In order to adjust the focal length and expand the field angle, the characteristic surface shapes of the DMs composed of the low‐order and high‐order Zernike polynomials were provided. The maximum peak‐to‐valley (PV) value at the center of the surface shape is 80 μm. This work presents a piezoelectrically‐actuated deformable mirror (PADM) with large displacement for a stabilized zoom system. The COMSOL simulation was conducted to obtain the desired design by matching and optimizing structural parameters. The maximum displacement of PADM was more than 80 μm. The fitting results by the steepest descent algorithm (SD) showed that the RMS values of the residual surface shapes were 1 of 7 to 1 of 4 of their PV values, and the PV values of the residual surface shapes were less than 1 of 10 of the PV values of the target surface shapes.

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“…(2) Gesture control algorithm based on Hidden Markov Model (HMM). [37][38][39][40][41] HMM is an important probabilistic model for statistical learning and sequence data processing. A Hidden Markov Model-based gesture recognition algorithm corresponds to an HMM model during training, and the classification with the highest probability is the recognition result.…”

Section: Introductionmentioning

confidence: 99%

An interactive gesture control system for collaborative manipulator based on Leap Motion Controller

Li,

Lou,

Cai

et al. 2024

Advances in Mechanical Engineering

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Gesture control is often used for the control of robotic manipulators. However conventional methods always focus on position control of robotic manipulators and use a fixed position to place gesture detection devices, which limits the flexibility to control and interact. This paper presents an interactive gesture control system based on Leap Motion Controller that can overcome these shortcomings. A coordinate transformation is performed between the position of the left palm detected by Leap Motion Controller and the tool center point (TCP) of the collaborative manipulator to obtain gesture data first. This gesture data is used to control the posture of a six-joint collaborative manipulator-Lite6. The position of gripper is controlled by grip level of the right palm. A controller is designed to manipulate a lift table on which Leap Motion Controller is placed to achieve adaptive ascending and descending movements to meet the needs of operators of different heights and arm spans. A modified Kalman filter is used to filter noise and smooth the signal captured by Leap Motion Controller. The experiments demonstrate that the system can operate stably and accurately control the position, posture, and gripper position of a collaborative manipulator in real time using human palms’ gestures.

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Grasping detection of dual manipulators based on Markov decision process with neural network

Cited by 8 publications

References 46 publications

Unsupervised method for detecting surface defects in steel based on joint optimization of pseudo-labeling and clustering

Unsupervised method for detecting surface defects in steel based on joint optimization of pseudo-labeling and clustering

Design of novel deformable mirror with large displacement

An interactive gesture control system for collaborative manipulator based on Leap Motion Controller

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