Implementación de un sistema de búsqueda de rutas de evacuación eficientes ante la presencia de sismos en un centro comercial usando el algoritmo D estrella

Descripción del Articulo

Around the world there are different authors that have shown big interest related to natural disasters such as earthquakes. This have generated different technological solutions around modern evacuation systems. This research explains why it is important to implement an intelligent evacuation system...

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Detalles Bibliográficos
Autor: Pariona Sanchez, Walter Steven
Formato: tesis de grado
Fecha de Publicación:2023
Institución:Universidad de Lima
Repositorio:ULIMA-Institucional
Lenguaje:español
OAI Identifier:oai:repositorio.ulima.edu.pe:20.500.12724/19748
Enlace del recurso:https://hdl.handle.net/20.500.12724/19748
Nivel de acceso:acceso abierto
Materia:Terremotos
Algoritmos computacionales
Computer algorithms
Earthquakes
https://purl.org/pe-repo/ocde/ford#2.02.04
Descripción
Sumario:Around the world there are different authors that have shown big interest related to natural disasters such as earthquakes. This have generated different technological solutions around modern evacuation systems. This research explains why it is important to implement an intelligent evacuation system that can recognize the shortest path in a real earthquake. The research also reviews the different modern evacuation systems being used nowadays. In this way, after many phases of analysis, a proof of concept was built to find the shortest path in the first level of a shopping mall using the D star search algorithm. Furthermore, a comparison between the A star algorithm and D star algorithm was made to find out the performance of these. It was found that the A star algorithm finds the shortest path in 3 milliseconds, but it doesn’t support scenarios with dynamic paths. Thus, it was found that D start algorithm can support paths with dynamic obstacles and find the shortest path in 24 milliseconds, making it the most accurate algorithm for real evacuations scenarios. Ultimately, in order to evaluate the performance of the system, the experimental time was measured when the system calculates the shortest path in different environments such as: maps with scales of 5, 10 and 15, different number of existing obstacles and different number of obstacles generated in real time. The results of this research were favorable since the system was able to find an efficient shortest path in 22 milliseconds. Also, the simulator found an alternative path in 3 milliseconds for scenarios in which new obstacles were introduced in the path.
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