In this study, we introduce a method for estimating the position of a self-driving solar panel-cleaning mobile robot. This estimation relies on line counts, typically 16 cm in panel width, obtained through image processing on the panel floor, along with wheel encoder information and inertial sensor data. To achieve accurate line counts, we introduce two adjusted threshold values and allow offsets in these values based on the robot's speed. Additionally, inertial measurement unit (IMU) signals assist in determining whether a line is horizontal or vertical, depending on the robot's movement direction on the panel, utilizing the robot's heading angle and detected line angle. When the robot is positioned between lines on the panel, more precise location estimation is necessary beyond simple line counts. To tackle this challenge, we integrate the extended Kalman filter with IMU data and encoder information, significantly enhancing position estimation. This integration achieves an RMSE accuracy value of up to 0.089 m, notably at a relatively high speed of 100 mm/s. This margin of error is almost half that of the vision-based line-counting method.
Keywords: Vision processing; extended Kalman filter; localization; sensor fusion; solar panel-cleaning robot.