Intrinsic and environmental factors modulating autonomous robotic search under high uncertainty
EntityUAM. Departamento de Ingeniería Informática
10.1038/s41598-021-03826-3Scientific Reports 11.1 (2021): 24509
Funded byThis 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
ProjectGobierno de España. Grants PGC2018-095895-B-I00; Gobierno de España. TIN2017-84452-R; Gobierno de España. PID2020-114867RB-I00
Rights© The author(s)
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.
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
Google Scholar:Garcia-Saura, Carlos - Serrano Jerez, Eduardo - Rodríguez Ortiz, Francisco Borja - Varona Martínez, Pablo
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