Robust Multi-Sensor Fusion for Dynamic Robotic Grasping Using Fuzzy Adaptive Extended Kalman Filter
Precise target localization and tracking are fundamental prerequisites for robotic arm grasping tasks in dynamic environments. Traditional single-sensor systems, such as depth cameras, are susceptible to environmental interference, particularly under varying illumination and complex backgrounds. To solve this problem, this paper proposes a novel lightweight multi-sensor fusion framework combining 2D LiDAR and depth camera data using a Fuzzy Adaptive Extended Kalman Filter (FA-EKF). Unlike standard EKF methods that use a fixed observation noise covariance matrix , the proposed FA-EKF utilizes a fuzzy logic controller to dynamically adjust the noise covariance based on real-time measurement innovations. This allows the system to robustly handle non-linearities, sensor degradation, and time-varying noise. In the visual perception module, an improved YOLOv8 equipped with a Convolutional Block Attention Module (CBAM) is utilized to enhance feature extraction. Meanwhile, the LiDAR module extracts cylindrical targets using a robust RANSAC-based geometric fitting algorithm rather than traditional least-squares. Experimental validations performed on an AUBO-i5 robotic arm tracking a hovering coaxial drone demonstrate that the proposed FA-EKF method reduces the Root Mean Square Error (RMSE) to 0.0038m, and achieves a grasping success rate of 96% even under low-light conditions. The proposed system offers a real-time and highly robust solution for dynamic robotic grasping.
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