Improving the Accuracy of Cable-Driven Parallel Robots Through Model Optimization and Machine-Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-45705-0_55
Reference16 articles.
1. Akhmetzyanov, A. et al.: Deep learning with transfer learning method for error compensation of cable-driven robot. In: ICINCO (2020)
2. Chawla, I et al.: Neural network-based inverse kineto-static analysis of cable-driven parallel robot considering cable mass and elasticity. In: Cable-Driven Parallel Robots: Proceedings of the 5th International Conference on Cable-Driven Parallel Robots. pp. 50–62. Springer (2021)
3. Choi, S.-H., Park, K.-S.: The integrated elasto-plastic cable modeling for cable driven parallel robots (CDPRs). In: 2017 17th International Conference on Control, Automation and Systems (ICCAS). pp. 420–422. IEEE (2017)
4. Fabritius, M et al.: A framework for analyzing the accuracy, complexity, and long-term performance of cable-driven parallel robot models. In: Mechanism and Machine Theory, vol 185, pp. 105331. (2023) ISSN: 0094-114X
5. Fabritius, M. et al.: A nullspace-based force correction method to improve the dynamic performance of cable-driven parallel robots. In: Mechanism and Machine Theory, vol 181. pp. 105177. (2023) ISSN: 0094-114X
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