%0 Conference Paper %1 shuva2022reduced %A Shuva, Shahnewaz %A Buchfink, Patrick %A Röhrle, Oliver %A Haasdonk, Bernard %B Large-Scale Scientific Computing %D 2022 %E Lirkov, Ivan %E Margenov, Svetozar %I Springer International Publishing %K PN5 EXC2075 PN5-7 selected %P 402--409 %T Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue %X We present efficient reduced basis (RB) methods for the simulation of a coupled problem consisting of a rigid robot hand interacting with soft tissue material. The soft tissue is modeled by the linear elasticity equation and discretized with the Finite Element Method. We look at two different scenarios: (i) the forward simulation and (ii) a feedback control formulation of the model. In both cases, large-scale systems of equations appear, which need to be solved in real-time. This is essential in practice for the implementation in a real robot. For the feedback-scenario, we encounter a high-dimensional Algebraic Riccati Equation (ARE) in the context of the linear quadratic regulator. To overcome the real-time constraint by significantly reducing the computational complexity, we use several structure-preserving and non-structure-preserving reduction methods. These include reduced basis techniques based on the Proper Orthogonal Decomposition. For the ARE, we compute a low-rank-factor and hence solve a low-dimensional ARE instead of solving a full dimensional problem. Numerical examples for both cases (i) and (ii) are provided. These illustrate the approximation quality of the reduced solution and speedup factors of the different reduction approaches. %@ 978-3-030-97549-4

@inproceedings{shuva2022reduced, abstract = {We present efficient reduced basis (RB) methods for the simulation of a coupled problem consisting of a rigid robot hand interacting with soft tissue material. The soft tissue is modeled by the linear elasticity equation and discretized with the Finite Element Method. We look at two different scenarios: (i) the forward simulation and (ii) a feedback control formulation of the model. In both cases, large-scale systems of equations appear, which need to be solved in real-time. This is essential in practice for the implementation in a real robot. For the feedback-scenario, we encounter a high-dimensional Algebraic Riccati Equation (ARE) in the context of the linear quadratic regulator. To overcome the real-time constraint by significantly reducing the computational complexity, we use several structure-preserving and non-structure-preserving reduction methods. These include reduced basis techniques based on the Proper Orthogonal Decomposition. For the ARE, we compute a low-rank-factor and hence solve a low-dimensional ARE instead of solving a full dimensional problem. Numerical examples for both cases (i) and (ii) are provided. These illustrate the approximation quality of the reduced solution and speedup factors of the different reduction approaches.}, added-at = {2024-03-26T11:56:22.000+0100}, author = {Shuva, Shahnewaz and Buchfink, Patrick and Röhrle, Oliver and Haasdonk, Bernard}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/25b89403a8f724160194492c3d4a310f7/exc2075}, booktitle = {Large-Scale Scientific Computing}, editor = {Lirkov, Ivan and Margenov, Svetozar}, interhash = {265d41292d6ac75fea2eea1080339eb2}, intrahash = {5b89403a8f724160194492c3d4a310f7}, isbn = {978-3-030-97549-4}, keywords = {PN5 EXC2075 PN5-7 selected}, pages = {402--409}, publisher = {Springer International Publishing}, timestamp = {2024-03-26T11:56:22.000+0100}, title = {Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue}, year = 2022 }