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Intrinsic and environmental factors modulating autonomous robotic search under high uncertainty

Author
Garcia-Saura, Carlos; Serrano Jerez, Eduardountranslated; Rodríguez Ortiz, Francisco Borjauntranslated; Varona Martínez, Pablountranslated
Entity
UAM. Departamento de Ingeniería Informática
Publisher
Springer Nature
Date
2021-12-31
Citation
10.1038/s41598-021-03826-3
Scientific Reports 11.1 (2021): 24509
 
 
 
ISSN
2045-2322 (online)
DOI
10.1038/s41598-021-03826-3
Funded by
This work was supported in part by Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER), under Grants PGC2018-095895-B-I00, TIN2017-84452-R, and PID2020-114867RB-I00
Project
Gobierno de España. PGC2018-095895-B-I00; Gobierno de España. TIN2017-84452-R; Gobierno de España. PID2020-114867RB-I00
Editor's Version
https://doi.org/10.1038/s41598-021-03826-3
Subjects
Informática
URI
http://hdl.handle.net/10486/701448
Rights
© The author(s)

Licencia Creative Commons
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.

Abstract

Autonomous robotic search problems deal with different levels of uncertainty. When uncertainty is low, deterministic strategies employing available knowledge result in most effective searches. However, there are domains where uncertainty is always high since information about robot location, environment boundaries or precise reference points is unattainable, e.g., in cave, deep ocean, planetary exploration, or upon sensor or communications impairment. Furthermore, latency regarding when search targets move, appear or disappear add to uncertainty sources. Here we study intrinsic and environmental factors that affect low-informed robotic search based on diffusive Brownian, naive ballistic, and superdiffusive strategies (Lévy walks), and in particular, the effectiveness of their random exploration. Representative strategies were evaluated considering both intrinsic (motion drift, energy or memory limitations) and extrinsic factors (obstacles and search boundaries). Our results point towards minimum-knowledge based modulation approaches that can adjust distinct spatial and temporal aspects of random exploration to lead to effective autonomous search under uncertainty
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Google™ Scholar:Garcia-Saura, Carlos - Serrano Jerez, Eduardo - Rodríguez Ortiz, Francisco Borja - Varona Martínez, Pablo

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  • Producción científica en acceso abierto de la UAM [17777]

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