A Clustering Approach to Path Planning for Big Groups

Jakub Szkandera, Ondřej Kaas and Ivana Kolingerová
Additional contact information
Jakub Szkandera: University of West Bohemia, Pilsen, Czech Republic
Ondřej Kaas: University of West Bohemia, Pilsen, Czech Republic
Ivana Kolingerová: University of West Bohemia, Pilsen, Czech Republic

International Journal of Data Warehousing and Mining (IJDWM), 2019, vol. 15, issue 2, 42-61

Abstract: The article introduces a new method of planning paths for big groups in dynamic environments represented by a graph of vertices and edges, where the edge weight as well as the graph topology may change, but the method is also applicable to environments with a different representation. The utilization of clustering enables the use of a computed path for a group of agents. In this way, a speed-up and memory savings are achieved at a cost of some path sub-optimality. Examples of proper applications of the suggested approach are crowd simulation in urban environments or path-planning-based tasks in molecular biology. The experiments showed good behaviour of the method to speed-up, relative error and online computation.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJDWM.2019040103 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jdwm00:v:15:y:2019:i:2:p:42-61

Access Statistics for this article

International Journal of Data Warehousing and Mining (IJDWM) is currently edited by Eric Pardede

More articles in International Journal of Data Warehousing and Mining (IJDWM) from IGI Global
Bibliographic data for series maintained by Journal Editor ().