无人机在未知动态环境中实时避障，靠视觉直接规划路径。
SPOT: Spatio-Temporal Obstacle-free Trajectory Planning for UAVs in Unknown Dynamic Environments
- 用4维时空规划+视觉安全走廊，无需地图
- 实测在仿真和真实飞行中表现优于现有方法
- 新增应急避障模块，解决路径堵塞问题
针对四旋翼无人机在未知动态环境中的反应式运动规划问题，本文提出一种基于4维时空规划的方法，结合基于视觉的安全飞行走廊生成与轨迹优化。与依赖地图融合的先前方法不同，本框架为无地图设计，可直接从感知数据实现碰撞规避，并降低计算开销。通过基于视觉的物体分割与跟踪管道检测并追踪动态障碍物，实现场景中静态与动态元素的鲁棒分类。为进一步增强鲁棒性，引入备用规划模块，在无法直达目标时主动规避动态障碍物，缓解死锁状态下的碰撞风险。我们在仿真与真实硬件实验中对方法进行了全面验证，并与最先进方法进行对比，结果表明该方法在动态未知环境中具有显著优势。
原文摘要 · Abstract (English)
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.
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