Abstract
In this paper we introduce a neural network-based approach to tensegrity morphing: the task of actively changing the shape of a tensegrity structure to “fit” between obstacles in a cluttered environment. We specifically focus on a class of forming tasks, when the robot is required to pass between two parallel plate-like obstacles, and develop a robust solution both for generating dataset and for training predictor. Proposed predictor is able to predict both the shape of the tensegrity structure and the desired rest lengths of the actuated elastic elements, which can serve as motor commands when a quasi-static configuration-space trajectory tracking is used. We demonstrate high accuracy on validation dataset, and show the conditions when predictor overfits.
The research is supported by grant of the Russian Science Foundation (project No: 19-79-10246).
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References
Bruce, J., et al.: Superball: exploring tensegrities for planetary probes. In: 12th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS) (2014)
Hustig-Schultz, D., SunSpiral, V., Teodorescu, M.: Morphological design for controlled tensegrity quadruped locomotion. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4714–4719. IEEE (2016)
Luo, J., Edmunds, R., Rice, F., Agogino, A.M.: Tensegrity robot locomotion under limited sensory inputs via deep reinforcement learning. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 6260–6267. IEEE (2018)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Fürnkranz, J., Joachims, T. (eds.) Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807–814 (2010)
Paul, C., Roberts, J.W., Lipson, H., Cuevas, F.V.: Gait production in a tensegrity based robot. In: 2005 Proceedings of 12th International Conference on Advanced Robotics, ICAR 2005, pp. 216–222. IEEE (2005)
Rennie, C.M.: Designing and learning CPG gaits for spherical tensegrity robots using Bayesian optimization. Ph.D. thesis, Rutgers University-School of Graduate Studies (2018)
Sabelhaus, A.P., et al.: System design and locomotion of superball, an untethered tensegrity robot. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 2867–2873. IEEE (2015)
Savin, S., Balakhnov, O., Klimchik, A.: Energy-based local forward and inverse kinematics methods for tensegrity robots. In: 2020 Fourth IEEE International Conference on Robotic Computing (IRC), pp. 280–284. IEEE (2020)
Savin, S., Balakhnov, O., Maloletov, A.: Linearization-based forward kinematic algorithm for tensegrity structures with compressible struts. In: Ronzhin, A., Shishlakov, V. (eds.) Proceedings of 15th International Conference on Electromechanics and Robotics “Zavalishin’s Readings’’. SIST, vol. 187, pp. 293–303. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5580-0_24
Zalyaev, E., Savin, S., Vorochaeva, L.: Machine learning approach for tensegrity form finding: feature extraction problem. In: 2020 4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR), pp. 265–268. IEEE (2020)
Zhang, M., et al.: Deep reinforcement learning for tensegrity robot locomotion. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 634–641. IEEE (2017)
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Zalyaev, E., Savin, S. (2021). Tensegrity Morphing: Machine Learning-Based Tensegrity Deformation Predictor for Traversing Cluttered Environments. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 633. Springer, Cham. https://doi.org/10.1007/978-3-030-85910-7_50
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DOI: https://doi.org/10.1007/978-3-030-85910-7_50
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