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Affiliation

SIS-ASTROS GMF Project, Federal University of Santa Maria, Av. Roraima 1000, Santa Maria - RS, Brazil

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1 Papers

Hierarchical Terrain Representation and Flood Fill-based Computation of Large-Scale Terrain Changes for Agent-based Simulations

Topics: Domain-Specific Tools; Computer Simulation Techniques; Agent-Directed Simulation; Technologies, Tools and Platforms

Authors: Luis A. L. Silva ^{1

;

2} , Evaristo J. Nascimento ^{1

;

2} , Eliakim Zacarias ² , Raul C. Nunes ^{1

;

2} and Edison P. Freitas ^{3

;

2}

Affiliations: ¹ Graduate Program in Computer Science, Federal University of Santa Maria, Av. Roraima 1000, Santa Maria - RS, Brazil , ² SIS-ASTROS GMF Project, Federal University of Santa Maria, Av. Roraima 1000, Santa Maria - RS, Brazil , ³ Informatics Institute, Federal University of Rio Grande do Sul, Av. Bento Gonçalves 9500, Porto Alegre - RS, Brazil

Digital Object Identifier: 10.5220/0011134800003274

Proceedings of SIMULTECH 2022, ISBN: 978-989-758-578-4

Quick Abstract

Modern virtual training benefits from the recent advances in Agent-Based Modelling and Simulation (ABMS), making it possible to use real-world dynamic terrain scenarios that enhance the users learning from agent-based simulations. An important issue for distributed ABMS systems is the possibility of using terrain services that promptly compute large-scale terrain map changes as a result of natural phenomena such as river floods and wildfires. Performing the alterations in terrain maps is challenging since they depend on the combination of terrain features and terrain sizes. To address this problem, this work proposes the use flood fill-based techniques along with the hierarchical QuadTree approach for the terrain representation. We show that these techniques are essential to promptly compute the effects of the changes on a large number of nodes of the hierarchical map representation that captures the terrain features in different levels of detail. Also, a way to store and recover the QuadTree nodes in/from a dictionary-based memory is proposed, improving the nodes’ refinement and restoration process when the terrain changes are required on the simulations. Experiments with the proposed techniques show encouraging results, with reduced computing times considering terrains with different characteristics and numbers of alterations.