Research of Flexible Assembly of Miniature Circuit Breakers Based on Robot Trajectory Optimization

Han, Yiping; Shu, Liang; Wu, Ziran; Chen, Xuan; Zhang, Gaoyan; Cai, Zexiang

doi:10.3390/a15080269

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Sci. 2023, 13, x FOR PEER REVIEW 7 of 16 4. Fine registration is used to improve the precision of the results obtained by rough matching.…”

Section: Preprocessingmentioning

confidence: 99%

“…We propose a flexible-assembly method that clamps multiple types of internal components internal components and fixes them into the case via several industrial robots. In ou components of different types are mixed and randomly placed on the loading car the position and posture adjustment for the components is the first stage of the ass procedure [4].…”

Section: Introductionmentioning

confidence: 99%

“…Then, the positioning carrier transmitted to the next stage, and the components can be easily picked and fixed i MCB case. In the adjustment stage, to precisely pick and adjust each component loading carrier with one of the specially designed grippers of the claw at the end components of different types are mixed and randomly placed on the loading ca the position and posture adjustment for the components is the first stage of the a procedure [4].…”

Section: Introductionmentioning

confidence: 99%

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A Stereo-Vision-Based Spatial-Positioning and Postural-Estimation Method for Miniature Circuit Breaker Components

Zhang²,

Wang

et al. 2023

Applied Sciences

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This paper proposes a stereo-vision-based method that detects and registers the positions and postures of muti-type, randomly placed miniature circuit breaker (MCB) components within scene point clouds acquired by a 3D stereo camera. The method is designed to be utilized in the flexible assembly of MCBs to improve the precision of gripping small-sized and complex-structured components. The proposed method contains the following stages: First, the 3D computer-aided design (CAD) models of the components are converted to surface point cloud models by voxel down-sampling to form matching templates. Second, the scene point cloud is filtered, clustered, and segmented to obtain candidate-matching regions. Third, point cloud features are extracted by Intrinsic Shape Signatures (ISSs) from the templates and the candidate-matching regions and described by Fast Point Feature Histogram (FPFH). We apply Sample Consensus Initial Alignment (SAC-IA) to the extracted features to obtain a rough matching. Fourth, fine registration is performed by employing Iterative Closest Points (ICPs) with a K-dimensional Tree (KD-tree) between the templates and the roughly matched targets. Meanwhile, Random Sample Consensus (RANSAC), which effectively solves the local optimal problem in the classic ICP algorithm, is employed to remove the incorrectly matching point pairs for further precision improvement. The experimental results show that the proposed method achieves spatial positioning errors smaller than 0.2 mm and postural estimation errors smaller than 0.5°. The precision and efficiency meet the requirements of the robotic flexible assembly for MCBs.

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“…Sci. 2023, 13, x FOR PEER REVIEW 7 of 16 4. Fine registration is used to improve the precision of the results obtained by rough matching.…”

Section: Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Stereo-Vision-Based Spatial-Positioning and Postural-Estimation Method for Miniature Circuit Breaker Components

Zhang²,

Wang

et al. 2023

Applied Sciences

Self Cite

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A hybrid numerical/machine learning model development to improve the bimetal performance in the electric circuit breakers

Mallah

Aljuraid

Alawi

et al. 2022

Sci Rep

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Bimetals are widely used as a thermal tripping mechanism inside the miniature circuit breakers (MCBs) products when an overload current passes through the circuit for a certain period. Experimental, numerical, and, recently artificial intelligence methods are widely used in designing electric components. However, developing the bimetal for MCB products somewhat differs from developing other conductor items since they are strongly related to the electrical, mechanical, and thermal performance of the MCB. The conventional experimental and numerical approaches are time-consuming processes that cannot be easily utilized in optimizing the product's performance within the development lead time. In this study, a simple, fast, robust, and accurate novel methodology has been introduced to predict the temperature rise of the bimetal and other related performance characteristics. The numerical model has been built on the time-based finite difference method to frame the theoretical thermal model of the bimetal. Then, the numerical model has been consolidated by the machine learning (ML) model to take advantage of the experiments to provide an accurate, fast and reliable model finally. The novel model agrees well with the experimental tests, where the maximum error does not exceed 8%. The model has been used to redesign the bimetal of a 32 A MCB product and significantly reduce the maximum temperature by 24 °C. The novel model is promising since it considerably reduces the required design time, provides accurate predictions, and helps to optimize the performance of the circuit breaker products.

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Efficient and precise docking trajectory optimization for the ship block assembly

Li,

Chen,

Zhou

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part M:

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The assembly of the ship block is an extremely important stage of the shipbuilding process. Nevertheless, currently, the manual assembly efficiency is low, the accuracy is poor, and collision is very easy to occur. Therefore, there is an urgent need to conduct technical research on the automatic docking of ship blocks. The core of the automated docking technology is the attitude estimation and the trajectory planning of the posturing equipment. However, current data measurement and point set matching methods lead to large attitude-estimation errors, and it is difficult to meet the accuracy requirements of the assembly. Moreover, the current ship block trajectory planning methods pay more attention to single metrics, for example, time or energy consumption, while omitting the shock degree. In response to the above, this study first proposes a high-precision matching method for measuring point sets, in order to estimate the exact attitude of the ship block. Subsequently, trajectory translation for the block is performed using the seventh-degree polynomial. On this basis, a nonlinear weighted improved particle swarm optimization (IPSO) method is proposed to optimize the time, energy consumption and shock degree in the ship block trajectory planning process. Finally, the accuracy of the matching optimization is validated by simulation analysis and it is concluded that the seventh-degree polynomial leads to less shock than other polynomials. Furthermore, the shock force does not change abruptly even when the ship block is poised in steps. Through IPSO, the energy consumption and shock degree performance indices are optimized by 37.07% and 50.06%, respectively, in the ship block translation process.

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Research of Flexible Assembly of Miniature Circuit Breakers Based on Robot Trajectory Optimization

Cited by 5 publications

References 18 publications

A Stereo-Vision-Based Spatial-Positioning and Postural-Estimation Method for Miniature Circuit Breaker Components

A Stereo-Vision-Based Spatial-Positioning and Postural-Estimation Method for Miniature Circuit Breaker Components

A hybrid numerical/machine learning model development to improve the bimetal performance in the electric circuit breakers

Efficient and precise docking trajectory optimization for the ship block assembly

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