Abstract
As the issue of unauthorized drone flights becomes increasingly severe, the demand for intelligent counter-drone systems is growing. Existing fixed-deployment counter-drone systems suffer from limitations such as restricted coverage and passive defense. This study proposes integrating Autonomous Mobile Robot (AMR) into the Counter-Unmanned Aerial Systems (C-UAS) architecture to develop a mobile solution with active inspection capabilities. First, a kinematic model was established to describe the mutual mapping between the motor speed and the robot speed. Second, a manual mapping method was introduced; by integrating the Extended Kalman Filter (EKF) algorithm to fuse multi-sensor data, this method enables centimeter-level positioning. Finally, the system was integrated and verified in both simulated and real-world environments. Experimental results show that the Active C-UAS can successfully navigate to the drone intrusion area while achieving high positioning accuracy, path quality, and control performance. Specifically, the Mean Absolute Error(MAE) of the actual navigation target point is only 0.35 m, and the attitude estimation error is less than 1°. Moreover, the system maintains excellent trajectory smoothness in long-distance dynamic scenarios, achieving 89.9% of the Smoothness Coefficient (SMC) benchmark from global planning while only incurring a 1.14% increase in actual path length. Finally, high control accuracy and rapid response are confirmed by an MAE below 0.02 m/s between the commanded and actual speeds, alongside a Pearson correlation coefficient ([Formula: see text]) exceeding 0.9 between the two speed profiles.