abbast

• Qvale, Paul; Andersen, Marius Endre & Tariverdi, Abbas (2026). A physics-based and a data-driven approach to weld bead geometry modeling. Welding in the World. ISSN 0043-2288. 70, s. 3851–3860. doi: 10.1007/s40194-026-02461-8. Fulltekst i vitenarkiv Vis sammendrag

  Welding processes involve complex physical phenomena, of which accurate modeling can be computationally expensive. Still, good predictions of weld bead geometries are necessary for optimal planning of robotic welding. In the current work, a two-dimensional model for welding in a groove assuming parabolic weld bead profiles was established. The scale and orientation of the parabolas were determined from the welding parameters and direction of gravity. Another model employed a purely empirical neural-network approach, in which the bead profiles had a more flexible geometric parametrization consisting of a set of radial distances from the welding torch. This enabled the representation of more complex bead profiles. Calibration and validation of the two models were based on three welding tests with welding directions varying from 0 to 45° compared to the horizontal direction. The correspondence between the modeled and measured bead profiles was good for both models, with the neural network having the highest accuracy, especially in the transitions between bead and substrate. However, further validation and generalization to data sets with larger variation in welding parameters are needed.

• Sajadi, Seyedmohammadreza; Tariverdi, Abbas; Brun, Henrik; Elle, Ole Jakob & Mathiassen, Kim (2026). Robotic transesophageal echocardiography: system design and deep learning-based kinematic modeling. Frontiers in Robotics and AI. 12. doi: 10.3389/frobt.2025.1705142. Fulltekst i vitenarkiv
• Tariverdi, Abbas; Côté-Allard, Ulysse Teller Masao; Mathiassen, Kim; Elle, Ole Jakob; Kalvøy, Håvard & Martinsen, Ørjan Grøttem [Vis alle 7 forfattere av denne artikkelen] (2023). Reinforcement Learning-Based Switching Controller for a Milliscale Robot in a Constrained Environment. IEEE Transactions on Automation Science and Engineering. ISSN 1545-5955. 21(2), s. 2000–2016. doi: 10.1109/TASE.2023.3259905. Fulltekst i vitenarkiv
• Tariverdi, Abbas; Mathiassen, Kim; Søyseth, Vegard; Kalvøy, Håvard; Elle, Ole Jakob & Tørresen, Jim [Vis alle 8 forfattere av denne artikkelen] (2022). Physics-Based Simulation and Control Framework for Steering a Magnetically-Actuated Guidewire. I Dilger, Lale Canan (Red.), 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665496070. doi: 10.1109/CoDIT55151.2022.9803963. Fulltekst i vitenarkiv
• Tariverdi, Abbas; Venkiteswaran, Venkatasubramanian Kalpathy; Richter, Michiel; Elle, Ole Jakob; Tørresen, Jim & Mathiassen, Kim [Vis alle 8 forfattere av denne artikkelen] (2021). A Recurrent Neural-Network-Based Real-Time Dynamic Model for Soft Continuum Manipulators. Frontiers in Robotics and AI. doi: 10.3389/frobt.2021.631303. Fulltekst i vitenarkiv Vis sammendrag

  This paper introduces and validates a real-time dynamic predictive model based on a neural network approach for soft continuum manipulators. The presented model provides a real-time prediction framework using neural-network-based strategies and continuum mechanics principles. A time-space integration scheme is employed to discretize the continuous dynamics and decouple the dynamic equations for translation and rotation for each node of a soft continuum manipulator. Then the resulting architecture is used to develop distributed prediction algorithms using recurrent neural networks. The proposed RNN-based parallel predictive scheme does not rely on computationally intensive algorithms; therefore, it is useful in real-time applications. Furthermore, simulations are shown to illustrate the approach performance on soft continuum elastica, and the approach is also validated through an experiment on a magnetically-actuated soft continuum manipulator. The results demonstrate that the presented model can outperform classical modeling approaches such as the Cosserat rod model while also shows possibilities for being used in practice.

• Tariverdi, Abbas; Venkiteswaran, Venkatasubramanian Kalpathy; Martinsen, Ørjan Grøttem; Elle, Ole Jacob; Tørresen, Jim & Misra, Sarthak (2020). Dynamic modeling of soft continuum manipulators using lie group variational integration. PLOS ONE. 15:e0236121(7), s. 1–29. doi: 10.1371/journal.pone.0236121. Fulltekst i vitenarkiv Vis sammendrag

  This paper presents the derivation and experimental validation of algorithms for modeling and estimation of soft continuum manipulators using Lie group variational integration. Existing approaches are generally limited to static and quasi-static analyses, and are not sufficiently validated for dynamic motion. However, in several applications, models need to consider the dynamical behavior of the continuum manipulators. The proposed modeling and estimation formulation is obtained from a discrete variational principle, and therefore grants outstanding conservation properties to the continuum mechanical model. The main contribution of this article is the experimental validation of the dynamic model of soft continuum manipulators, including external torques and forces (e.g., generated by magnetic fields, friction, and the gravity), by carrying out different experiments with metal rods and polymer-based soft rods. To consider dissipative forces in the validation process, distributed estimation filters are proposed. The experimental and numerical tests also illustrate the algorithm’s performance on a magnetically-actuated soft continuum manipulator. The model demonstrates good agreement with dynamic experiments in estimating the tip position of a Polydimethylsiloxane (PDMS) rod. The experimental results show an average absolute error and maximum error in tip position estimation of 0.13 mm and 0.58 mm, respectively, for a manipulator length of 60.55 mm.

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