This study proposes an innovative approach to enhance the autonomous navigation accuracy of robots in poultry farms. It combines multiple positioning technologies, including the inertial navigation system (INS), real-time kinematic global navigation satellite system (RTK-GNSS), ultra-wideband (UWB) positioning, and simultaneous localization and mapping (SLAM), utilizing the extended Kalman filter (EKF) for integration. Besides, a novel weighted method is proposed for merging sensor fusion-based positioning with RTK-GNSS to maintain high accuracy, even in the absence of RTK-GNSS signals. Designed to operate within the robot operating system framework, the efficacy of the proposed system is evaluated in a simulated poultry farm environment. Experimental results reveal that proposed scheme significantly reduces the average positioning error to below 34 cm, offering a robust solution in GNSS-denied areas.
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Chang et al. (2024) studied this question.
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