Recursive fusion estimation for mobile robot localization under multiple energy harvesting sensors

Abstract This paper is concerned with the recursive fusion estimation‐based mobile robot localization (RL) problem by employing multiple energy harvesting sensors (EHSs). In the addressed RL problem, multiple sensors with energy harvesting capacity are deployed to produce measurements used for RL. W...

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Autores principales: Yanyang Lu, Hamid Reza Karimi
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Lenguaje:EN
Publicado: Wiley 2022
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Acceso en línea:https://doaj.org/article/ca74324522ec49d48c36f5d67b6ab2f4
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spelling oai:doaj.org-article:ca74324522ec49d48c36f5d67b6ab2f42021-12-02T15:00:29ZRecursive fusion estimation for mobile robot localization under multiple energy harvesting sensors1751-86521751-864410.1049/cth2.12201https://doaj.org/article/ca74324522ec49d48c36f5d67b6ab2f42022-01-01T00:00:00Zhttps://doi.org/10.1049/cth2.12201https://doaj.org/toc/1751-8644https://doaj.org/toc/1751-8652Abstract This paper is concerned with the recursive fusion estimation‐based mobile robot localization (RL) problem by employing multiple energy harvesting sensors (EHSs). In the addressed RL problem, multiple sensors with energy harvesting capacity are deployed to produce measurements used for RL. When the sensors own sufficient energy, the sensors can output measurements and then send them to the corresponding local filter. Otherwise, the sensor energy‐induced missing measurement phenomenon will occur. In order to obtain the missing measurement rate, at each time instant, the relationship between the totality of the sensor energy and its probability distribution is derived recursively. This paper aims at seeking out a practicable solution to the addressed mobile RL problem. First, in the presence of the sensor energy‐induced measurement missing phenomenon, an upper bound (UB) of the local localization error covariance is recursively acquired. Then, such a derived UB is minimized by suitably devising the desired local filter parameter. Subsequently, the covariance intersection fusion method is adopted to achieve the addressed RL problem. In the end, a simulation is conducted to verify the practicability of the developed RL scheme.Yanyang LuHamid Reza KarimiWileyarticleControl engineering systems. Automatic machinery (General)TJ212-225ENIET Control Theory & Applications, Vol 16, Iss 1, Pp 20-30 (2022)
institution DOAJ
collection DOAJ
language EN
topic Control engineering systems. Automatic machinery (General)
TJ212-225
spellingShingle Control engineering systems. Automatic machinery (General)
TJ212-225
Yanyang Lu
Hamid Reza Karimi
Recursive fusion estimation for mobile robot localization under multiple energy harvesting sensors
description Abstract This paper is concerned with the recursive fusion estimation‐based mobile robot localization (RL) problem by employing multiple energy harvesting sensors (EHSs). In the addressed RL problem, multiple sensors with energy harvesting capacity are deployed to produce measurements used for RL. When the sensors own sufficient energy, the sensors can output measurements and then send them to the corresponding local filter. Otherwise, the sensor energy‐induced missing measurement phenomenon will occur. In order to obtain the missing measurement rate, at each time instant, the relationship between the totality of the sensor energy and its probability distribution is derived recursively. This paper aims at seeking out a practicable solution to the addressed mobile RL problem. First, in the presence of the sensor energy‐induced measurement missing phenomenon, an upper bound (UB) of the local localization error covariance is recursively acquired. Then, such a derived UB is minimized by suitably devising the desired local filter parameter. Subsequently, the covariance intersection fusion method is adopted to achieve the addressed RL problem. In the end, a simulation is conducted to verify the practicability of the developed RL scheme.
format article
author Yanyang Lu
Hamid Reza Karimi
author_facet Yanyang Lu
Hamid Reza Karimi
author_sort Yanyang Lu
title Recursive fusion estimation for mobile robot localization under multiple energy harvesting sensors
title_short Recursive fusion estimation for mobile robot localization under multiple energy harvesting sensors
title_full Recursive fusion estimation for mobile robot localization under multiple energy harvesting sensors
title_fullStr Recursive fusion estimation for mobile robot localization under multiple energy harvesting sensors
title_full_unstemmed Recursive fusion estimation for mobile robot localization under multiple energy harvesting sensors
title_sort recursive fusion estimation for mobile robot localization under multiple energy harvesting sensors
publisher Wiley
publishDate 2022
url https://doaj.org/article/ca74324522ec49d48c36f5d67b6ab2f4
work_keys_str_mv AT yanyanglu recursivefusionestimationformobilerobotlocalizationundermultipleenergyharvestingsensors
AT hamidrezakarimi recursivefusionestimationformobilerobotlocalizationundermultipleenergyharvestingsensors
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