In complex environments, autonomous navigation for quadrotor drones presents challenges in terms of obstacle avoidance and path planning. Traditional artificial potential field (APF) methods are plagued by issues such as getting stuck in local minima and inadequate handling of dynamic obstacles. This paper introduces a layered obstacle avoidance structure that merges vortex artificial potential (VAPF) fields with reinforcement learning (RL) for motion control. This approach dynamically adjusts the target position through VAPF, strategically guiding the drone to avoid obstacles indirectly. Additionally, it employs the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to facilitate the training of the motion controller. Simulation experiments demonstrate that the incorporation of the VAPF effectively mitigates the issue of local minima and significantly enhances the success rate of drone navigation, reduces the average arrival time and the number of sharp turns, and results in smoother paths. This solution harmoniously combines the flexibility of VAPF methods with the precision of RL for motion control, offering an effective strategy for autonomous navigation of quadrotor drones in complex environments.

Despite the extensive research conducted in autonomous aerial confrontation, most of these studies have focused on homogeneous Unmanned Aerial Vehicles (UAVs). Heterogeneous aerial confrontation, represented by Manned-Unmanned Teaming (MUM-T), still faces two major challenges: (i) The lack of exploration into the underlying mechanisms of human-machine collaborative combat, which makes it difficult to fully leverage the respective advantages of manned aerial vehicles and UAVs; (ii) The absence of fully interpretable safety strategies limits the practical applicability of existing research findings. To address these challenges, this paper proposes a fully interpretable MUM-T framework for beyond-visual-range aerial confrontation, driven by State-Event-Condition-Action (SECA) rules. This framework introduces, for the first time in the context of MUM-T aerial confrontation, a comprehensive mechanism for target allocation and guidance task allocation at the multi-fighter level. Additionally, at the single-fighter level, this framework designs a weapon launch strategy based on value-distance assessment and differentiated decision-making rules. Based on the SECA methodology, this paper proposes a verification approach for rule correctness and validates the proposed rules. In simulated engagements against three typical tactics, the proposed framework demonstrates favorable performance in combat effectiveness, resource management, and airspace control. Typical case analysis further confirms the full interpretability. Furthermore, the framework's overall effectiveness is systematically validated through parameter advantage analysis, complexity and scalability analysis, and robustness analysis.