Tensegrity Morphing: Machine Learning-Based Tensegrity Deformation Predictor for Traversing Cluttered Environments - Advances in Production Management Systems Access content directly
Conference Papers Year : 2021

Tensegrity Morphing: Machine Learning-Based Tensegrity Deformation Predictor for Traversing Cluttered Environments

Eduard Zalyaev
  • Function : Author
  • PersonId : 1172291
Sergei Savin
  • Function : Author
  • PersonId : 1172292

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.
Fichier principal
Vignette du fichier
520761_1_En_50_Chapter.pdf (601.89 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03806478 , version 1 (07-10-2022)

Licence

Attribution

Identifiers

Cite

Eduard Zalyaev, Sergei Savin. Tensegrity Morphing: Machine Learning-Based Tensegrity Deformation Predictor for Traversing Cluttered Environments. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2021, Nantes, France. pp.473-480, ⟨10.1007/978-3-030-85910-7_50⟩. ⟨hal-03806478⟩
26 View
5 Download

Altmetric

Share

Gmail Facebook X LinkedIn More