Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-12889
Authors: Hägele, David
Abdelaal, Moataz
Oguz, Ozgur S.
Toussaint, Marc
Weiskopf, Daniel
Title: Visual analytics for nonlinear programming in robot motion planning
Issue Date: 2022
metadata.ubs.publikation.typ: Zeitschriftenartikel
metadata.ubs.publikation.seiten: 127-141
metadata.ubs.publikation.source: 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
Abstract: 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.
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