%0 Conference Paper %1 warsewa_multi-modal_2018 %A Warsewa, Alexander %A Sawodny, Oliver %A Tarin-Sauer, Cristina %A Guerra, Flavio %A Rapp, Philipp %A Haist, Tobias %A Osten, Wolfgang %B 13th World Congress on Computational Mechanics (WCCM), July 22-27, 2018, New York, NY, USA %D 2018 %K imported %T Multi-modal sensor fusion for state estimation of an adaptive structures test bench %X In this contribution, we consider the problem of state estimation for adaptive truss structures equipped with amultitude of sensors and actuators spanning different domains 1. For reliable actuation of such a system,knowledge of the system state, consisting of the building deformation that can be quantified by the spatial deviationof certain building points, is required. However, in a general application, the state is not usually fully measureable,which is why state estimation techniques have to be employed 2. A scale model of an adaptive truss structure withadditional plates is available as an experimentation platform in our laboratory as a scaled version of an actualmulti-story building. It is equipped with strain gauges, inertial measurement units and an optical measurementsystem. The optical measurement system consists of several light emitters, which are attached to the structure’souter faces and point to a remote camera. Using computer generated holograms and a multi-image technique,measurement of the emitter position is achieved with high precision. Sensor fusion is carried out and a completestate estimation is performed. Results obatained by an existing simulation model are experimentally validated onthe test bench using actual measurement data. 1 W. Sobek and P. Teuffel, “Adaptive systems in architecture andstructural engineering,” Proceedings of SPIE, vol. 4330, 2001, pp. 36–45. 2 P. Rapp, M. Heidingsfeld, M. Böhm,O. Sawodny, and C. Tarín, “Multimodal Sensor Fusion of Inertial, Strain, and Distance Data for State Estimation ofAdaptive Structures using Particle Filtering”, Proceedings of the 2017 IEEE/ASME International Conference onAdvanced Intelligent Mechatronics (AIM 2017), 2017.

@inproceedings{warsewa_multi-modal_2018, abstract = {In this contribution, we consider the problem of state estimation for adaptive truss structures equipped with amultitude of sensors and actuators spanning different domains [1]. For reliable actuation of such a system,knowledge of the system state, consisting of the building deformation that can be quantified by the spatial deviationof certain building points, is required. However, in a general application, the state is not usually fully measureable,which is why state estimation techniques have to be employed [2]. A scale model of an adaptive truss structure withadditional plates is available as an experimentation platform in our laboratory as a scaled version of an actualmulti-story building. It is equipped with strain gauges, inertial measurement units and an optical measurementsystem. The optical measurement system consists of several light emitters, which are attached to the structure’souter faces and point to a remote camera. Using computer generated holograms and a multi-image technique,measurement of the emitter position is achieved with high precision. Sensor fusion is carried out and a completestate estimation is performed. Results obatained by an existing simulation model are experimentally validated onthe test bench using actual measurement data. [1] W. Sobek and P. Teuffel, “Adaptive systems in architecture andstructural engineering,” Proceedings of SPIE, vol. 4330, 2001, pp. 36–45. [2] P. Rapp, M. Heidingsfeld, M. Böhm,O. Sawodny, and C. Tarín, “Multimodal Sensor Fusion of Inertial, Strain, and Distance Data for State Estimation ofAdaptive Structures using Particle Filtering”, Proceedings of the 2017 IEEE/ASME International Conference onAdvanced Intelligent Mechatronics (AIM 2017), 2017.}, added-at = {2020-01-20T12:58:59.000+0100}, author = {Warsewa, Alexander and Sawodny, Oliver and Tarin-Sauer, Cristina and Guerra, Flavio and Rapp, Philipp and Haist, Tobias and Osten, Wolfgang}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/23588b85efb2a9706d2548e000c89a920/mgeiger}, booktitle = {13th {World} {Congress} on {Computational} {Mechanics} ({WCCM}), {July} 22-27, 2018, {New} {York}, {NY}, {USA}}, interhash = {24e31f5e84826cb3c100bf618bf18624}, intrahash = {3588b85efb2a9706d2548e000c89a920}, keywords = {imported}, note = {B02, A04, A06, ISYS, ITO}, timestamp = {2020-01-20T11:58:59.000+0100}, title = {Multi-modal sensor fusion for state estimation of an adaptive structures test bench}, year = 2018 }