Efficient and reliable UAV navigation in cluttered and dynamic environments remains challenging. We propose SWIFT-Nav: Stability-aware Waypoint-level Integration of Fuzzy arbitration and TD3 for Navigation, a TD3-based navigation framework that achieves fast, stable convergence to obstacle-aware paths. The system couples a sensor-driven perception front end with a TD3 waypoint policy: the perception module converts LiDAR ranges into a confidence-weighted safety map and goal cues, while the TD3 policy is trained with Prioritised Experience Replay to focus on high-error transitions and a decaying epsilon-greedy exploration schedule that gradually shifts from exploration to exploitation. A lightweight fuzzy-logic layer computes a safety score from radial measurements and near obstacles, gates mode switching and clamps unsafe actions; in parallel, task-aligned reward shaping combining goal progress, clearance, and switch-economy terms provides dense, well-scaled feedback that accelerates learning. Implemented in Webots with proximity-based collision checking, our approach consistently outperforms baselines in trajectory smoothness and generalization to unseen layouts, while preserving real-time responsiveness. These results show that combining TD3 with replay prioritisation, calibrated exploration, and fuzzy-safety rules yields a robust and deployable solution for UAV navigation in cluttered scenes.

翻译：在杂乱和动态环境中实现高效可靠的无人机导航仍然具有挑战性。我们提出SWIFT-Nav：一种基于稳定性感知的航点级模糊仲裁与TD3集成导航框架，这是一种基于TD3的导航框架，能够快速、稳定地收敛到障碍物感知路径。该系统将传感器驱动的感知前端与TD3航点策略耦合：感知模块将LiDAR测距数据转换为置信度加权的安全地图和目标线索，而TD3策略则通过优先经验回放进行训练，以聚焦于高误差转换，并采用衰减的epsilon-greedy探索调度，逐步从探索转向利用。一个轻量级的模糊逻辑层根据径向测量和邻近障碍物计算安全分数，控制模式切换并限制不安全动作；同时，结合目标进展、避障距离和切换经济性的任务对齐奖励塑形提供了密集且尺度适当的反馈，加速了学习过程。在Webots中实现并采用基于接近度的碰撞检测，我们的方法在轨迹平滑度和对未见布局的泛化能力上持续优于基线方法，同时保持了实时响应性。这些结果表明，将TD3与回放优先级、校准探索和模糊安全规则相结合，为无人机在杂乱场景中的导航提供了一个鲁棒且可部署的解决方案。