让无人机在突发障碍前预判风险，实时调整安全路径。
RA-Nav: A Risk-Aware Navigation System Based on Semantic Segmentation for Aerial Robots in Unpredictable Environments
- 通过语义分割识别障碍物类型，分三类建模风险。
- 实测在突发障碍场景下成功率显著优于基线方法。
- 适合复杂动态环境下的无人机自主导航应用。
现有空中机器人导航系统通常针对静态与动态障碍规划路径，但在静态障碍突然移动时无法适应。融合环境语义信息可评估突发移动障碍带来的潜在风险。本文提出基于语义分割的鲁棒风险感知导航框架RA-Nav。采用轻量级多尺度语义分割网络实时识别障碍物类别，并进一步分为静止、暂时静止和动态三类。针对每类设计相应风险估计函数，构建完整局部风险地图。基于该地图，设计风险感知路径搜索算法，平衡路径效率与安全性；再通过轨迹优化生成安全、平滑且动态可行的轨迹。对比仿真显示，在突发障碍状态转换场景中，RA-Nav的成功率显著高于基线方法。其有效性还通过真实世界数据仿真进一步验证。
原文摘要 · Abstract (English)
Existing aerial robot navigation systems typically plan paths around static and dynamic obstacles, but fail to adapt when a static obstacle suddenly moves. Integrating environmental semantic awareness enables estimation of potential risks posed by suddenly moving obstacles. In this paper, we propose RA- Nav, a risk-aware navigation framework based on semantic segmentation. A lightweight multi-scale semantic segmentation network identifies obstacle categories in real time. These obstacles are further classified into three types: stationary, temporarily static, and dynamic. For each type, corresponding risk estimation functions are designed to enable real-time risk prediction, based on which a complete local risk map is constructed. Based on this map, the risk-informed path search algorithm is designed to guarantee planning that balances path efficiency and safety. Trajectory optimization is then applied to generate trajectories that are safe, smooth, and dynamically feasible. Comparative simulations demonstrate that RA-Nav achieves higher success rates than baselines in sudden obstacle state transition scenarios. Its effectiveness is further validated in simulations using real- world data.
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