RA-Nav: A Risk-Aware Navigation System Based on Semantic Segmentation for Aerial Robots in Unpredictable Environments

TL;DR

RA- Nav is proposed, a risk-aware navigation framework based on semantic segmentation that achieves higher success rates than baselines in sudden obstacle state transition scenarios and is validated in simulations using real- world data.

Abstract

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.

Figures (14)

• Figure 1: Framework of the semantic-enhanced navigation system. The perception and planning modules run asynchronously on the onboard computer. The perception module uses U-Net for semantic segmentation and updates a local map at fixed intervals. The planning module then uses the risk grid map for safe path search and optimization.
• Figure 2: Semantic segmentation network architecture, composed of a 2D backbone and a 3D semantic segmentation head, which enables multi-scale semantic segmentation.
• Figure 3: $(a)$ is the risk illustration of the static obstacle, its center is $(0,0)$ and its width and length are 5. $(b)$ is the risk illustration of the dynamic obstacle, its center is $(0,0)$ and its width and length are 2. The risk along the velocity of the dynamic obstacle is higher than the opposite direction.
• Figure 4: Local coordinate system centered at the obstacle. When $\Delta > 0$, the position lies in front (quadrants I and IV); when $\Delta < 0$, the position is behind (quadrants II and III).
• Figure 5: Initialization of control points. When the risk value of an obstacle exceeds the threshold $R_{thresh}$, the control points are shifted along the direction of risk reduction.