A new hierarchical approach for the optimal design of a 5-dof hybrid serial-parallel kinematic machine

Zhao, Yanqin; Mei, Jianping; Jin, Yan; Niu, Wentie

doi:10.1016/j.mechmachtheory.2020.104160

Cited by 19 publications

(3 citation statements)

References 31 publications

(33 reference statements)

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“…4, coordinate systems are applied on the serial mechanism to establish kinematic model. According to D-H method, the link relationship of serial mechanism can be described by four kinematic parameters [8], namely, link angle  , link offset d , link length a and joint torsion angle  , which are as shown in Table 1.…”

Section: Kinematic Analysis and Verificationmentioning

confidence: 99%

Topological design and kinematic analysis of a novel hybrid space control mechanism

Yang

Wang

Liu

et al. 2022

J. Phys.: Conf. Ser.

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In order to meet the demand of space mechanism for Chinese space station, a novel hybrid space control mechanism is designed. The hybrid space control mechanism is made up of a serial mechanism and a parallel mechanism. The serial mechanism realizes large workspace and the parallel mechanism provides excellent position accuracy. The physical prototype of the novel hybrid space control mechanism is built up, and the mobility and foldability of the serial mechanism are exhibited. Its kinematic and workspace analysis are carried out, which correctness is verified through comparison with simulation results. The aforementioned research laid a solid foundation for future study about the hybrid space control mechanism.

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Section: Kinematic Analysis and Verificationmentioning

confidence: 99%

Topological design and kinematic analysis of a novel hybrid space control mechanism

Yang

Wang

Liu

et al. 2022

J. Phys.: Conf. Ser.

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“…Industrial parallel robots are widely used to pick up boxing and sort items in production lines [1], but they have difficulties in * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

RP-YOLOX-DL: a deep learning hybrid method for parallel robots target positioning

Zhang

Wang

et al. 2023

Meas. Sci. Technol.

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To address the issues of low classification pickup efficiency and slow response time of parallel robots positioning methods based on machine vision, this paper proposes a deep learning hybrid method RP-YOLOX-DL (YOLOX-Deeplabv3+ method for robots positioning) for parallel robots to achieve accurate picking. Firstly, the RP-YOLOX lightweight network is used to complete target recognition classification and rough positioning. A new feature enhanced network called DS-PANet is proposed to optimize the original up-and-down sampling structure, and the computational efficiency is improved through the attention mechanism and deep convolution. The loss function in network evaluation is enhanced, and an emphasizing the target Binary CrossEntropy loss function (ETBCE) is proposed for the objective loss strategy. Secondly, the Deeplabv3+(DL) network is used and the pooling structure is improved to obtain rich multi-scale information by using different types of Atrous convolution. Then, the extracted semantic segmentation center coordinates are finely positioned, and by using a hybrid positioning strategy, the RP-YOLOX and DL network modules are combined to obtain the best positioning coordinates of the target. Finally, a hand-eye calibration operation was performed to convert the robot, the camera, and the conveyor belt to eye-to-hand coordinate computation. The experimental results indicate that the hybrid method achieves a pick-up rate of 92.56% and a response time of 2.357s, showing better performance than the traditional algorithms Faster-RCNN, YOLOv3, and YOLOv5. Meanwhile, the identification efficiency is increased by 2.41% compared to the YOLOX algorithm. These results verify the efficiency and robust adaptability of the mixed method. This study has a certain reference value for applying the deep learning method to robots positioning pick-up.

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“…Starting from the stability and safety of the opening and closing state of the disconnector, this paper establishes the mathematical model and physical model of the disconnector. Through the kinematic analysis [18][19][20], the parametric design [21,22] and optimization analysis of the drive system are carried out by using ADAMS (Automatic Dynamic Analysis of Mechanical Systems). At the same time, the PSO algorithm which is often used to solve optimization problems is selected to verify the reliability of the optimization results [23].…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of High-Voltage Disconnecting Switch Drive System Based on ADAMS and Particle Swarm Optimization Algorithm

et al. 2021

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This paper focuses on the analysis of the stability of the GW17 high-voltage disconnecting switch drive system. Firstly, the optimization model of the disconnector is established, and the simulation analysis is carried out by ADAMS (Automatic Dynamic Analysis of Mechanical Systems) and the simulation results are verified by experiments. Afterwards, ADAMS optimization design and particle swarm optimization algorithm (PSO) are used to optimize the drive system of the disconnector, and the results are verified on the experimental platform. After optimization, the space rod is reduced by 15 mm, the minimum corner angle of the lower conductive rod is reduced by 71.0%, the minimum folding arm angle is reduced by 88.7% and the maximum force of the ball pair is reduced by 35.7%, which realizes the lightweight of the rod, reduces the wear of the ball pair, and improves the stability of the equipment operation.

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A new hierarchical approach for the optimal design of a 5-dof hybrid serial-parallel kinematic machine

Cited by 19 publications

References 31 publications

Topological design and kinematic analysis of a novel hybrid space control mechanism

Topological design and kinematic analysis of a novel hybrid space control mechanism

RP-YOLOX-DL: a deep learning hybrid method for parallel robots target positioning

Optimal Design of High-Voltage Disconnecting Switch Drive System Based on ADAMS and Particle Swarm Optimization Algorithm

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