Mobile Robot Localization in Indoor Environments Using Fuzzy Adaptive Unscented Kalman Filter and Random Tree Routing Algorithm with Fast Exploration

سال انتشار: 1404
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 24

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شناسه ملی سند علمی:

JR_IECO-8-3_005

تاریخ نمایه سازی: 24 شهریور 1404

چکیده مقاله:

In the field of mobile robot navigation, challenges such as nonlinear conditions, uncertainties, and the development ‎of advanced methods have necessitated accurate position estimation. In this paper, fuzzy based adaptive ‎unscented Kalman filter (FAUKF) is evaluted to enhance the state estimation performance for mobile robot ‎localization. In proposed method, the FAUKF algorithm effectively removes the noise uncertainty by adaptively ‎adjusting the covariance of the measurement noise according to the adaptation law. The output of the Mamdani ‎Fuzzy Inference System (FIS) acts as an observer and improves the matching law. The results of this research show ‎the accuracy of FAUKF algorithm position estimation compared to traditional UKF methods. Also, this study ‎presents a pioneering navigation strategy for mobile robots, which integrates random tree routing algorithm with ‎rapid exploration (RRT*) for optimal path design in indoor environments. The goal of RRT* integration is to create ‎optimal routes taking into account safety and environmental constraints. By combining the Kalman filter ‎prediction and updating steps, this method reduces the accumulation of uncertainty during the localization process ‎and facilitates accurate localization and path planning towards the target.‎The simulation results confirm the effectiveness of this method in keeping the uncertainty levels in localization ‎constant over time. The presented adaptive method enables efficient navigation in complex environments. Path ‎planning is a critical element in robotics applications and the RRT* based approach presented in this paper provides ‎a comprehensive solution to create optimal and efficient paths.

کلیدواژه ها:

Fuzzy ، ‎ Random tree routing ‎ Robot localization ، ‎ Unscented Kalman filter

نویسندگان

Mohammadrasol Hajali

Faculty of Electrical Engineering and Computer, University of Birjand, Iran

Ramezan Havangi

Faculty of Electrical Engineering and Computer, University of Birjand, Iran