Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
157 tokens/sec
GPT-4o
43 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

CafkNet: GNN-Empowered Forward Kinematic Modeling for Cable-Driven Parallel Robots (2402.18420v4)

Published 28 Feb 2024 in cs.RO

Abstract: Cable-driven parallel robots (CDPRs) have gained significant attention due to their promising advantages. When deploying CDPRs in practice, the kinematic modeling is a key question. Unlike serial robots, CDPRs have a simple inverse kinematics problem but a complex forward kinematics (FK) issue. So, the development of accurate and efficient FK solvers has been a prominent research focus in CDPR applications. By observing the topology within CDPRs, in this paper, we propose a graph-based representation to model CDPRs and introduce CafkNet, a fast and general FK solving method, leveraging Graph Neural Network (GNN) to learn the topological structure and yield the real FK solutions with superior generality, high accuracy, and low time cost. CafkNet is extensively tested on 3D and 2D CDPRs in different configurations, both in simulators and real scenarios. The results demonstrate its ability to learn CDPRs' internal topology and accurately solve the FK problem. Then, the zero-shot generalization from one configuration to another is validated. Also, the sim2real gap can be bridged by CafkNet using both simulation and real-world data. To the best of our knowledge, it is the first study that employs the GNN to solve the FK problem for CDPRs.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (33)
  1. S. Qian, B. Zi, W.-W. Shang, and Q.-S. Xu, “A review on cable-driven parallel robots,” Chinese Journal of Mechanical Engineering, vol. 31, no. 1, pp. 1–11, 2018.
  2. Z. Zhang, Z. Shao, Z. You, X. Tang, B. Zi, G. Yang, C. Gosselin, and S. Caro, “State-of-the-art on theories and applications of cable-driven parallel robots,” Frontiers of Mechanical Engineering, vol. 17, no. 3, p. 37, 2022.
  3. M. Zarebidoki, J. S. Dhupia, and W. Xu, “A review of cable-driven parallel robots: Typical configurations, analysis techniques, and control methods,” IEEE Robotics & Automation Magazine, vol. 29, no. 3, pp. 89–106, 2022.
  4. B. Zhang, W. Shang, X. Gao, Z. Li, X. Wang, Y. Ma, F. Zhang, R. Yao, H. Li, J. Yin, et al., “Synthetic design and analysis of the new feed cabin mechanism in five-hundred-meter aperture spherical radio telescope (fast),” Mechanism and Machine Theory, vol. 191, p. 105507, 2024.
  5. A. Pott, “Forward kinematics and workspace determination of a wire robot for industrial applications,” Advances in Robot Kinematics: Analysis and Design, pp. 451–458, 2008.
  6. C. B. Pham, S. H. Yeo, G. Yang, M. S. Kurbanhusen, and I.-M. Chen, “Force-closure workspace analysis of cable-driven parallel mechanisms,” Mechanism and Machine Theory, vol. 41, no. 1, pp. 53–69, 2006.
  7. Z. Zhang, H. H. Cheng, and D. Lau, “Efficient wrench-closure and interference-free conditions verification for cable-driven parallel robot trajectories using a ray-based method,” IEEE Robotics and Automation Letters, vol. 5, no. 1, pp. 8–15, 2019.
  8. B. Zhang, B. Deng, X. Gao, W. Shang, and S. Cong, “Design and implementation of fast terminal sliding mode control with synchronization error for cable-driven parallel robots,” Mechanism and Machine Theory, vol. 182, p. 105228, 2023.
  9. A. Pott and V. Schmidt, “On the forward kinematics of cable-driven parallel robots,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2015, pp. 3182–3187.
  10. U. A. Mishra and S. Caro, “Forward kinematics for suspended under-actuated cable-driven parallel robots with elastic cables: A neural network approach,” Journal of Mechanisms and Robotics, vol. 14, no. 4, p. 041008, 2022.
  11. X. Wang, B. Zhang, W. Shang, and S. Cong, “Optimal reconfiguration planning of a 3-dof point-mass cable-driven parallel robot,” IEEE Transactions on Industrial Electronics, 2023.
  12. Z. Zhang, “Ray-based interference free workspace analysis and path planning for cable-driven robots,” arXiv preprint arXiv:2211.07612, 2022.
  13. Y. Liu, Z. Cao, H. Xiong, J. Du, H. Cao, and L. Zhang, “Dynamic obstacle avoidance for cable-driven parallel robots with mobile bases via sim-to-real reinforcement learning,” IEEE Robotics and Automation Letters, vol. 8, no. 3, pp. 1683–1690, 2023.
  14. A. Akhmetzyanov, M. Rassabin, A. Maloletov, M. Fadeev, and A. Klimchik, “Model free error compensation for cable-driven robot based on deep learning with sim2real transfer learning,” in 17th International Conference on Informatics in Control, Automation and Robotics.   Springer, 2022, pp. 479–496.
  15. K. Wang, W. R. Johnson, S. Lu, X. Huang, J. Booth, R. Kramer-Bottiglio, M. Aanjaneya, and K. Bekris, “Real2sim2real transfer for control of cable-driven robots via a differentiable physics engine,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2023, pp. 2534–2541.
  16. E. Ottaviano, M. Ceccarelli, and P. Pelagalli, “A performance analysis of a 4 cable-driven parallel manipulator,” in 2006 IEEE Conference on Robotics, Automation and Mechatronics.   IEEE, 2006, pp. 1–6.
  17. J.-P. Merlet, “Advances in the use of neural network for solving the direct kinematics of cdpr with sagging cables,” in International Conference on Cable-Driven Parallel Robots.   Springer, 2023, pp. 30–39.
  18. H. Hong, J. Ali, and L. Ren, “A review on topological architecture and design methods of cable-driven mechanism,” Advances in mechanical engineering, vol. 10, no. 5, p. 1687814018774186, 2018.
  19. S. Zare, M. S. Haghighi, M. R. H. Yazdi, A. Kalhor, and M. T. Masouleh, “Kinematic analysis of an under-constrained cable-driven robot using neural networks,” in 2020 28th Iranian Conference on Electrical Engineering (ICEE).   IEEE, 2020, pp. 1–6.
  20. Y. Lu, S. Lin, G. Chen, and J. Pan, “Modlanets: learning generalisable dynamics via modularity and physical inductive bias,” in International Conference on Machine Learning.   PMLR, 2022, pp. 14 384–14 397.
  21. L. Yang, B. Huang, Q. Li, Y.-Y. Tsai, W. W. Lee, C. Song, and J. Pan, “Tacgnn: Learning tactile-based in-hand manipulation with a blind robot using hierarchical graph neural network,” IEEE Robotics and Automation Letters, vol. 8, no. 6, pp. 3605–3612, 2023.
  22. S. Funabashi, T. Isobe, F. Hongyi, A. Hiramoto, A. Schmitz, S. Sugano, and T. Ogata, “Multi-fingered in-hand manipulation with various object properties using graph convolutional networks and distributed tactile sensors,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 2102–2109, 2022.
  23. J. T. Kim, J. Park, S. Choi, and S. Ha, “Learning robot structure and motion embeddings using graph neural networks,” arXiv preprint arXiv:2109.07543, 2021.
  24. A. Sanchez-Gonzalez, N. Heess, J. T. Springenberg, J. Merel, M. Riedmiller, R. Hadsell, and P. Battaglia, “Graph networks as learnable physics engines for inference and control,” in International Conference on Machine Learning.   PMLR, 2018, pp. 4470–4479.
  25. K. R. Allen, T. L. Guevara, Y. Rubanova, K. Stachenfeld, A. Sanchez-Gonzalez, P. Battaglia, and T. Pfaff, “Graph network simulators can learn discontinuous, rigid contact dynamics,” in Conference on Robot Learning.   PMLR, 2023, pp. 1157–1167.
  26. T. Wang, R. Liao, J. Ba, and S. Fidler, “Nervenet: Learning structured policy with graph neural networks,” in International conference on learning representations, 2018.
  27. J. Whitman, M. Travers, and H. Choset, “Learning modular robot control policies,” IEEE Transactions on Robotics, 2023.
  28. H. Sun, L. Yang, Y. Gu, J. Pan, F. Wan, and C. Song, “Bridging locomotion and manipulation using reconfigurable robotic limbs via reinforcement learning,” Biomimetics, vol. 8, no. 4, p. 364, 2023.
  29. X. Ji, H. Li, Z. Pan, X. Gao, and C. Tu, “Decentralized, unlabeled multi-agent navigation in obstacle-rich environments using graph neural networks,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2021, pp. 8936–8943.
  30. P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al., “Relational inductive biases, deep learning, and graph networks,” arXiv preprint arXiv:1806.01261, 2018.
  31. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
  32. R. Liaw, E. Liang, R. Nishihara, P. Moritz, J. E. Gonzalez, and I. Stoica, “Tune: A research platform for distributed model selection and training,” arXiv preprint arXiv:1807.05118, 2018.
  33. P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, et al., “Scipy 1.0: fundamental algorithms for scientific computing in python,” Nature methods, vol. 17, no. 3, pp. 261–272, 2020.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets