Gilbert–Johnson–Keerthi distance algorithm: Difference between revisions

Revision as of 02:21, 12 February 2023

Revision as of 05:42, 14 March 2023

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{{Short description|Method of determining minimum distance between two convex sets}}

The '''Gilbert–Johnson–Keerthi distance [[algorithm]]''' is a method of determining the minimum distance between two [[convex set]]s, first published by [[Elmer G. Gilbert]], Daniel W. Johnson, and S. Sathiya Keerthi in 19981988. Unlike many other distance algorithms, it does not require that the geometry data be stored in any specific format, but instead relies solely on a [[support function]] to iteratively generate closer [[simplex|simplices]] to the correct answer using the ''configuration space obstacle'' (CSO) of two convex shapes, more commonly known as the [[Minkowski difference]].

"Enhanced GJK" algorithms use edge information to speed up the algorithm by following edges when looking for the next simplex. This improves performance substantially for polytopes with large numbers of vertices.