Energy-efficient environment mapping via evolutionary algorithm optimized multi-agent localization

Authors:
Ahmed Hallawa, Stephan Schlupkothen, Giovanni Iacca, and Gerd Ascheid
Book Title:
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Series:
GECCO '17
Publisher:
ACM
Address:
New York, NY, USA
Pages:
1721–1726
Date:
July 2017
ISBN:
978-1-45034-939-0
DOI:
10.1145/3067695.3084201
Language:
English

BibTeX

@inproceedings{hallawa17a,
author = {Ahmed Hallawa, Stephan Schlupkothen, Giovanni Iacca, and Gerd Ascheid},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
title = {Energy-efficient environment mapping via evolutionary algorithm optimized multi-agent localization},
year = {2017},
month = {jul},
address = {New York, NY, USA},
pages = {1721-1726},
ISBN= {978-1-45034-939-0},
publisher = {ACM},
series = {GECCO '17},
doi = {10.1145/3067695.3084201},
}

Abstract

Miniature autonomous sensory agents (MASA) can play a profound role in the exploration of hardly accessible unknown environments, thus, impacting many applications such as monitoring of underground infrastructure or exploration for natural resources, e.g. oil and gas, or even human body diagnostic exploration. However, using MASA presents a wide range of challenges due to limitations of the available hardware resources caused by their scaled-down size. Consequently, these agents are kinetically passive, i.e. they cannot be guided through the environment. Furthermore, their communication range and rate is limited, which affects the quality of localization and, consequently, mapping. In addition, conducting real-time localization and mapping is not possible. As a result, Simultaneous Localization and Mapping (SLAM) techniques are not suitable and a new problem definition is needed. In this paper we introduce what we dub as the Centralized Offline Localization And Mapping (COLAM) problem, highlighting its key elements, then we present a model to solve it. In this model evolutionary algorithms (EAs) are used to optimize agents' resources off-line for an energy-efficient environment mapping. Furthermore, we illustrate a modified version of Vietoris-Rips Complex we dub as Trajectory Incorporated Vietoris-Rips (TIVR) complex as a tool to conduct mapping. Finally, we project the proposed model on real experiments and present results.

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