Mobile Robot Localization Using Fuzzy Segments

Herrero-Pérez, David; Alcaraz-Jiménez, Juan José; Barberá, Humberto Martínez

doi:10.5772/57224

Cited by 5 publications

(2 citation statements)

References 59 publications

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“…We have to deal with the uncertainty of such perceptions to maintain a coherent representation of the environment surrounding the underwater vehicle. We adopt a fuzzy segment framework [ 25 , 26 ] to represent and deal with the location uncertainty using line segments. These features include a representation of their uncertain location.…”

Section: Sensor Modelingmentioning

confidence: 99%

Sensor Modeling for Underwater Localization Using a Particle Filter

Barberá

Bernal-Polo

Herrero-Pérez

2021

Sensors

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This paper presents a framework for processing, modeling, and fusing underwater sensor signals to provide a reliable perception for underwater localization in structured environments. Submerged sensory information is often affected by diverse sources of uncertainty that can deteriorate the positioning and tracking. By adopting uncertain modeling and multi-sensor fusion techniques, the framework can maintain a coherent representation of the environment, filtering outliers, inconsistencies in sequential observations, and useless information for positioning purposes. We evaluate the framework using cameras and range sensors for modeling uncertain features that represent the environment around the vehicle. We locate the underwater vehicle using a Sequential Monte Carlo (SMC) method initialized from the GPS location obtained on the surface. The experimental results show that the framework provides a reliable environment representation during the underwater navigation to the localization system in real-world scenarios. Besides, they evaluate the improvement of localization compared to the position estimation using reliable dead-reckoning systems.

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Section: Sensor Modelingmentioning

confidence: 99%

Sensor Modeling for Underwater Localization Using a Particle Filter

Barberá

Bernal-Polo

Herrero-Pérez

2021

Sensors

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“…Alternatively, some other previously conducted studies focused on hybridizing the IFP and AFP to increase the uncertainty handling features of the positioning system, for example, [81][82][83]. Table 1 presents the fuzzy system usage within the localization problem based on the aforementioned classification.…”

Section: Classification Of Fuzzy Systems In Localizationmentioning

confidence: 99%

A survey of fuzzy logic in wireless localization

Alakhras

Oussalah

Hussein

2020

J Wireless Com Network

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Positioning and location estimation of targets either for mobile or stationary nodes is very important, especially after the astonishing developments of the wired and/or wireless communications, as well as the popularity of handheld devices. To assist in accessing services or managing resources, numerous users, application developers, and service providers require accurate position information. Owing to the boom of multisensory systems, wireless technology, and autonomous systems, many positioning systems have been proposed. These positioning systems differ based on the adopted technology, type of sensory information, communication protocol, algorithm, and accuracy level. These different approaches impose a non-negligible amount of uncertainty and challenges; in such case, fuzzy-logic-based localization systems offer an attractive and feasible approach to overcome some of the reported challenges and handle uncertainties, depending on its proven mathematical framework for handling vagueness and uncertainty. Intuitively, fuzzy sets and fuzzy inference systems are initially intended to incorporate granularity and flexibility of human knowledge, which characterize complex system behaviors without explicitly requiring precise mathematical models or when a complete and exhaustive list of comprehending factors are ill known. This study demonstrates the benefits of fuzzy sets, fuzzy logic, and fuzzy inference systems in wireless positioning problems. Notably, as a way to benefit both the academic community and practitioners of system positioning, we review and investigate various fuzzy-related techniques and methodologies. To the best of our knowledge, this is the first comprehensive review that focuses solely on fuzzy-based positioning systems.

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