Obstacle-Avoidance Guidance Algorithm Based on Adaptive Dynamic Programming*

Since the beginning of the 21st century, small unmanned aerial vehicles (UAVs) have emerged as primary equipment in multiple localized conflicts due to their flexibility, portability, and cost-effectiveness. During strike missions in low-altitude airspace, small UAVs frequently encounter obstacle interference, posing significant challenges to the environmental adaptability of guidance systems. To enhance strike capabilities in complex low-altitude environments, this paper investigates guidance algorithms with obstacle-avoidance capabilities. We propose an online Adaptive Dynamic Programming (ADP) method that dynamically adjusts obstacle-avoidance strategies using real-time environmental data and UAV/target kinematic states. This approach ensures safe obstacle navigation while maintaining optimal guidance performance. Numerical simulations verify the environmental adaptability of the proposed algorithm in obstacle-rich scenarios.