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.

With the rapid development of the aviation industry, the complexity and intertwinement of various aviation activities have been continuously increasing, leading to the escalating of potential conflicts in limited airspace resources. Efficient airspace management is crucial for flight safety. To address the issues of conflict detection and resolution in localized high-density multi-airspace environments, this paper proposes an end-to-end integrated framework that integrates octree spatial partitioning, hierarchical conflict detection, and a conflict resolution module based on the Improved Whale Migration Algorithm (IWMA). This framework takes the airspace safety separation standards as constraints and the minimization of multi-airspace adjustment offsets as the optimization objective. The IWMA integrates diverse population structures and multi-strategy optimization technologies to balance global search and local exploration capabilities and avoid falling into local optima. Experiments on high-density conflict scenarios of different scales have verified the effectiveness and robustness of the proposed method. Simulation results show that compared with traditional detection methods and other classic optimization algorithms, the conflict detection and resolution method proposed in this paper can quickly and efficiently provide reliable solutions for multi-airspace conflict issues.

Existing autonomous navigation systems for Unmanned Aerial Vehicles (UAVs) face the dual challenges of local minima entrapment and computational complexity that scales with environmental density. This paper proposes a hierarchical navigation architecture integrating deep representation learning with an improved Vortex Artificial Potential Field (APF). At the decision layer, a Convolutional Neural Network (CNN) encodes the environment as a fixed-dimensional tensor and generates global waypoints with constant-time inference, independent of obstacle count. At the control layer, a Vortex APF resolves the Goal Non-Reachable with Obstacles Nearby (GNRON) problem and limit-cycle oscillations through tangential rotational potentials, achieving significant improvement in trajectory smoothness compared to traditional APF methods. A closed-loop replanning mechanism further ensures robust performance under execution drift. Experiments across varying obstacle densities demonstrate that the combined system achieves high navigation success rates in dense environments with substantially reduced computation time compared to sampling-based planners such as Rapidly exploring Random Tree star (RRT*), while maintaining superior trajectory quality. This architecture provides a computationally efficient solution for resource-constrained UAV platforms operating in GPS-denied or obstacle-rich environments such as warehouses, forests, and disaster sites.