We address the problem of reactive motion planning for quadrotors operating in unknown environments with dynamic obstacles. Our approach leverages a 4-dimensional spatio-temporal planner, integrated with vision-based Safe Flight Corridor (SFC) generation and trajectory optimization. Unlike prior methods that rely on map fusion, our framework is mapless, enabling collision avoidance directly from perception while reducing computational overhead. Dynamic obstacles are detected and tracked using a vision-based object segmentation and tracking pipeline, allowing robust classification of static versus dynamic elements in the scene. To further enhance robustness, we introduce a backup planning module that reactively avoids dynamic obstacles when no direct path to the goal is available, mitigating the risk of collisions during deadlock situations. We validate our method extensively in both simulation and real-world hardware experiments, and benchmark it against state-of-the-art approaches, showing significant advantages for reactive UAV navigation in dynamic, unknown environments.

翻译：本文针对四旋翼无人机在未知动态障碍物环境中的反应式运动规划问题展开研究。我们提出了一种四维时空规划方法，该方法与基于视觉的安全飞行走廊生成及轨迹优化技术相集成。与现有依赖地图融合的方法不同，本框架无需构建地图，可直接基于感知实现避障，同时降低了计算开销。通过基于视觉的目标分割与跟踪流程，系统能够检测并追踪动态障碍物，从而实现对场景中静态与动态元素的鲁棒分类。为进一步增强系统鲁棒性，我们引入了备用规划模块，当不存在直达目标的路径时，该模块能反应式地规避动态障碍物，有效降低了死锁状态下的碰撞风险。我们在仿真与真实硬件实验中对该方法进行了广泛验证，并与前沿方法进行了性能对比，结果表明本方法在动态未知环境下的反应式无人机导航方面具有显著优势。