Mobile Robot using Kalman Filter for Localization
DOI:
https://doi.org/10.63278/10.63278/mme.v31.1Keywords:
Localization, Kalman Filter, Mobile Robot, Simulations, Simulink.Abstract
This research investigates using a Kalman filter to enhance the mobile robot localization. Accurate localization is crucial for effective steering and successful mission completion in the autonomous robotic systems. The Traditional methods encounter problems comes from the noise and the errors in sensor measurements, resulting in the failure in performance. The Kalman filter is a mathematical method that may guess the state of the dynamic system depend upon a sequence of noisy and the incomplete explanations. It is a consistent technique to address localization issues. The Kalman filter, a method for the iterative state estimation, significantly improves the localization precision of a two-wheeled (2WD) robot. The experimental outcomes display that the filter decreases uncertainty and enhance the robot's ability to cross the complex conditions. A Simulink application permits fast modeling and simulation of the robot's dynamic performance, giving valuable visions into perfect filter limitations. This research aims to improve the robotics arena by indicating a dependable method for the localization, which is vital for an autonomous steering and the mission execution. The numerical analysis has a significantly improves the robot's capacity to steer in the noisy environments, rendering it a possible explanation for the real-time applications in the dynamic situations.
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