Fast k-Nearest Neighbor on a Navigation Mesh

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Abstract

We consider the k-Nearest Neighbour problem in a two-dimensional Euclidean plane with obstacles (OkNN). Existing and state of the art algorithms for OkNN are based on incremental visibility graphs and as such suffer from a well known disadvantage: costly and online visibility checking with quadratic worst-case running times. In this work we develop a new OkNN algorithm which avoids these disadvantages by representing the traversable space as a collection of convex polygons; i.e. a Navigation Mesh. We then adapt an recent and optimal navigation mesh algorithm, Polyanya, from the single-source single-target setting to the the multi-target case. We also give two new heuristics for OkNN. In a range of empirical comparisons we show that our approach can be orders of magnitude faster than competing methods that rely on visibility graphs.
Original languageEnglish
Title of host publicationProceedings of the Eleventh International Symposium on Combinatorial Search
EditorsVadim Bulitko, Sabine Storandt
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages124-132
Number of pages8
ISBN (Electronic)9781577358022
Publication statusPublished - 2018
EventInternational Symposium on Combinatorial Search 2018 - Stockholm, Sweden
Duration: 14 Jul 201815 Jul 2018
Conference number: 11th
https://sites.google.com/view/socs18/

Conference

ConferenceInternational Symposium on Combinatorial Search 2018
Abbreviated titleSOCS 2018
CountrySweden
CityStockholm
Period14/07/1815/07/18
Internet address

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