Event-Triggered Image Moments Predictive Control for Tracking Evolving Features Using UAVs

This paper presents a novel approach for tracking deformable contour targets using Unmanned Aerial Vehicles (UAVs). The proposed scheme combines image moments descriptor and Event-Triggered (ET) Nonlinear Model Predictive Control (NMPC) for efficient and accurate tracking. The deformable contour model allows adaptation to the evolving target's shape, while the proposed event-triggered scheme achieves improved computational efficiency and extended flight duration while generating new control sequences for the UAV. Real-world experimental validation as well as a comparative simulation performance analysis validate the scheme, showcasing its robustness in handling complex scenarios. This approach holds promise for various applications, such as surveillance and autonomous navigation.