Fuzzy uncertainty modeling for grid based localization of mobile robots

Herrero-Perez, D.Dept Informat & Communicat Engn, Univ Murcia, Murcia, Spain (författare)

Volume 51, Issue 8, October 2010
This paper presents a localization method using fuzzy logic to represent the different facets of uncertainty present in sensor data. Our method follows the typical predict-update cycle of recursive state estimators to estimate the robot's location. The method is implemented on a fuzzy position grid, and several simplifications are introduced to reduce computational complexity. The main advantages of this fuzzy logic method compared to most current ones are: (i) only an approximate sensor model is required, (ii) several facets of location uncertainty can be represented, and (iii) ambiguities in the sensor information are directly represented, thus avoiding having to solve the data association problem separately. Our method has been validated experimentally on two different platforms, a legged robot equipped with vision and a wheeled robot equipped with range sensors. The experiments show that our method can solve both the tracking and the global localization problem. They also show that this method can successfully cope with ambiguous observations, when several features may be associated to the same observation, and with robot kidnapping situations. Additional experiments are presented that compare our approach with a state-of-the-art probabilistic method. (C) 2010 Elsevier Inc. All rights reserved.

Martinez-Barbera, H.Dept Informat & Communicat Engn, Univ Murcia, Murcia, Spain (författare)
LeBlanc, KevinÖrebro universitet,Akademin för naturvetenskap och teknik(Swepub:oru)klc (författare)
Saffiotti, AlessandroÖrebro universitet,Akademin för naturvetenskap och teknik(Swepub:oru)asaffio (författare)
Dept Informat & Communicat Engn, Univ Murcia, Murcia, SpainAkademin för naturvetenskap och teknik (creator_code:org_t)

Ingår i:International Journal of Approximate ReasoningAmsterdam : Elsevier, s. 912-9320888-613X

Av författaren/redakt...: Herrero-Perez, D ...; Martinez-Barbera ...; LeBlanc, Kevin; Saffiotti, Aless ...

Om ämnet

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.