Evidence map›Paper›PMID 41516479›Full record

ArticleSensors (Basel, Switzerland)2025

Kinematic Modeling and Solutions for Cable-Driven Parallel Robots Considering Adaptive Pulley Kinematics.

Zhonghua Hu, Chaowen Deng, Kai Wang, Jianqing Peng

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zhonghua HuSchool of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252000, China.ORCID 0000-0001-8312-4305
Chaowen DengSchool of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252000, China.
Kai WangCollege of Engineering, China Agricultural University, Beijing 100083, China.
Jianqing PengSchool of Intelligent Systems Engineering, Shenzhen Campus, Sun Yat-sen University, Shenzhen 518107, China.ORCID 0000-0002-9777-842X

Funding

Doctoral Startup Fund of Liaocheng University 318052231Guangdong Natural Science Foundation 2024A1515010540National Key R&D Program of China 2022YFB4703100National Natural Science Foundation of China 62303205Shandong Provincial Natural Science Foundation ZR2023QF137Shenzhen Science and Technology Program JCYJ20220530150006014University-Industry Collaborative Education Program 240506429202244
6 · The paper itself

Abstract

Although the use of adaptive pulleys enhances the motion characteristics of cable-driven parallel robots (CDPRs), it significantly increases the complexity of the kinematics model. Conventional methods often fail to account for the influence of adaptive pulley motion on cable length variation, making it difficult to establish a precise kinematics model. To deal with the problem, this study presents a kinematic modeling and solution method for CDPRs, which incorporates adaptive pulley kinematics. First, the structural design of the CDPR driven by eight cables is analyzed. Then, the generalized kinematics model and the improved kinematics model with adaptive pulley considerations are established. Furthermore, a hybrid Levenberg-Marquardt and Genetic algorithm is proposed to achieve the efficient and high-precision solution of kinematics equations by combining the rapid global search and precise local optimization. Finally, the proposed method is validated through straight path simulation and elliptical path simulation. The simulation results indicate that the tracking accuracy of the end-effector is better than the 1 × 10

Indexed as

adaptive pulley kinematicscable-driven parallel robothybrid Levenberg–Marquardt and Genetic algorithmkinematic modelingkinematic solution

Identifiers

PMID41516479
PMCPMC12787786

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.