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：本文研究了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。我们提出了一种融合基于视觉的安全飞行走廊生成与轨迹优化的四维时空规划方法。与依赖地图融合的现有方法不同，本框架无需构建地图，可直接根据感知信息实现碰撞规避，同时降低计算开销。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，实现对场景中静态与动态元素的鲁棒分类。为进一步增强鲁棒性，我们引入了备份规划模块，当不存在直达目标路径时，该模块能反应式规避动态障碍物，从而降低死锁状态下的碰撞风险。我们在仿真与实物硬件实验中进行了广泛验证，并与前沿方法进行对比测试，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。