<![CDATA[Robot localization is essential for successful navigation, particularly in indoor environments where Global Positioning System (GPS) devices are ineffective. Bluetooth Low Energy (BLE) beacons provide a promising solution by transmitting 2.4GHz signals that can be interpreted by nearby robots. The trilateration method, utilizing Received Signal Strength Indicator (RSSI) values from BLE beacons at predefined locations, enables position estimation. However, RSSI values are highly susceptible to fluctuations and environmental interference, leading to significant errors. This research addresses these challenges by developing a low-cost beacon device using an ESP32 microcontroller and implementing a Kalman filter to minimize RSSI fluctuations. A curve fitting method is applied to convert filtered RSSI data into distance estimates, offering improved accuracy compared to the path loss model. The trilateration approach determines the robot’s position based on three dominant BLE beacons, selected for their signal strength. Results demonstrate that the proposed localization system is effective, with the integration of the Kalman filter and beacon selection mechanism significantly enhancing positional accuracy. This study contributes to the advancement of indoor localization by providing a robust and cost-efficient system suitable for autonomous mobile robot navigation.]]>
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