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- W2617650030 abstract "Abstract To solve the problems in guidance, navigation, and control for an autonomous robot, its accurate positioning and localization are needed. Two or more different sensors are often used to obtain reliable data useful for control system. Extended Kalman Filter (EKF) is widely used to fuse those data to obtain one optimal result. The signals used during navigation cannot be always considered as white noise signals. On the other hand, colored signals will cause the EKF to diverge. This paper presents the data fusion system for mobile robot navigation. Odometry and sonar signals are fused using Extended Kalman Filter (EKF) and Adaptive Fuzzy Logic System (AFLS). The AFLS was used to adapt the gain and therefore prevent the Kalman filter divergence. The fused signal is more accurate than any of the original signals considered separately. The enhanced, more accurate signal is used to guide and navigate the robot." @default.
- W2617650030 created "2017-06-05" @default.
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- W2617650030 date "2000-09-01" @default.
- W2617650030 modified "2023-09-25" @default.
- W2617650030 title "Odometry and Sonar Data Fusion for Mobile Robot Navigation" @default.
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- W2617650030 doi "https://doi.org/10.1016/s1474-6670(17)37964-8" @default.
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