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http://dx.doi.org/10.18419/opus-12889
Autor(en): | Hägele, David Abdelaal, Moataz Oguz, Ozgur S. Toussaint, Marc Weiskopf, Daniel |
Titel: | Visual analytics for nonlinear programming in robot motion planning |
Erscheinungsdatum: | 2022 |
Dokumentart: | Zeitschriftenartikel |
Seiten: | 127-141 |
Erschienen in: | Journal of visualization 25 (2022), S. 127-141 |
URI: | http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-129084 http://elib.uni-stuttgart.de/handle/11682/12908 http://dx.doi.org/10.18419/opus-12889 |
ISSN: | 1343-8875 1875-8975 |
Zusammenfassung: | Nonlinear programming is a complex methodology where a problem is mathematically expressed in terms of optimality while imposing constraints on feasibility. Such problems are formulated by humans and solved by optimization algorithms. We support domain experts in their challenging tasks of understanding and troubleshooting optimization runs of intricate and high-dimensional nonlinear programs through a visual analytics system. The system was designed for our collaborators’ robot motion planning problems, but is domain agnostic in most parts of the visualizations. It allows for an exploration of the iterative solving process of a nonlinear program through several linked views of the computational process. We give insights into this design study, demonstrate our system for selected real-world cases, and discuss the extension of visualization and visual analytics methods for nonlinear programming. |
Enthalten in den Sammlungen: | 13 Zentrale Universitätseinrichtungen |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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s12650-021-00786-8.pdf | 1,6 MB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons