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

TL;DR

RA-Nav is a novel risk-aware aerial navigation system that uses semantic segmentation to classify obstacles and adapt paths in real-time, improving safety in unpredictable environments.

Contribution

The paper introduces RA-Nav, a framework combining semantic segmentation with risk estimation and path planning for aerial robots in dynamic scenarios.

Findings

RA-Nav outperforms baseline methods in success rates during sudden obstacle transitions.

Real-time semantic segmentation accurately classifies obstacle types.

Risk estimation enables safer and more efficient navigation in complex environments.

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.