🤖 AI Summary
This work addresses the challenges of unstable visual tracking, camera self-occlusion, and terminal impact-angle control encountered by fixed-wing unmanned aerial vehicles (UAVs) during the full “detect–track–strike” mission against moving targets. To overcome these issues, a three-stage cooperative strategy is proposed: initial target acquisition via YOLO-based vision fused with inertial measurements, stable tracking with self-occlusion avoidance using a constraint-aware nonlinear model predictive controller (NMPC) enhanced by control barrier functions (CBFs), and high-precision interception under impact-angle constraints through a switch to quaternion-based biased proportional navigation guidance (BPNG) upon satisfaction of terminal conditions. High-fidelity simulations demonstrate that the proposed framework significantly improves tracking robustness and terminal strike accuracy while rigorously respecting vehicle dynamics and field-of-view constraints.
📝 Abstract
This study introduces a unified control framework for fixed-wing unmanned aerial vehicles (UAVs) fitted with a pan-tilt (PT) camera, intended to perform an end-to-end mission spanning from initial target detection to accurate terminal engagement. The proposed system employs a three-phase strategy: a vision-based target acquisition phase, an NMPC-based tracking phase, and a terminal guidance phase. During tracking, the framework uses an Unscented Kalman Filter (UKF) to fuse YOLO-based visual detections with inertial measurements, enabling robust target state estimation under unknown dynamics. To ensure reliable visual contact, we introduce a constraint-aware Nonlinear Model Predictive Control (NMPC) strategy that incorporates Control Barrier Functions (CBFs) to explicitly prevent UAV self-occlusion -- a common limitation in fixed-wing tracking. Upon satisfying terminal engagement conditions, the system seamlessly transitions control to a quaternion-based Biased Proportional Navigation Guidance (BPNG) law, enforcing precise impact angle constraints. High-fidelity simulations demonstrate that the framework achieves stable, robust tracking and accurate terminal interception while strictly respecting the vehicle's dynamic limits and camera field-of-view constraints.