Robust navigation in changing marine environments requires autonomous systems capable of perceiving, reasoning, and acting under uncertainty. This study introduces a hybrid risk-aware navigation architecture that integrates probabilistic modeling of obstacles along the vehicle path with smooth trajectory optimization for autonomous surface vessels. The system constructs probabilistic risk maps that capture both obstacle proximity and the behavior of dynamic objects. A risk-biased Rapidly Exploring Random Tree (RRT) planner leverages these maps to generate collision-free paths, which are subsequently refined using B-spline algorithms to ensure trajectory continuity. Three distinct RRT* rewiring modes are implemented based on the cost function: minimizing the path length, minimizing risk, and optimizing a combination of the path length and total risk. The framework is evaluated in experimental scenarios containing both static and dynamic obstacles. The results demonstrate the system's ability to navigate safely, maintain smooth trajectories, and dynamically adapt to changing environmental risks. Compared with conventional LIDAR or vision-only navigation approaches, the proposed method shows improvements in operational safety and autonomy, establishing it as a promising solution for risk-aware autonomous vehicle missions in uncertain and dynamic environments.

翻译：在动态变化的海洋环境中实现鲁棒导航，要求自主系统具备在不确定性条件下进行感知、推理与行动的能力。本研究提出一种混合式风险感知导航架构，该架构将沿航迹的障碍物概率建模与自主水面艇的平滑轨迹优化相结合。系统构建了同时捕捉障碍物接近程度与动态物体行为的概率风险地图。基于风险偏好的快速探索随机树（RRT）规划器利用这些地图生成无碰撞路径，随后通过B样条算法对路径进行优化，确保轨迹连续性。依据代价函数的不同，实现了三种RRT*重连模式：最小化路径长度、最小化风险，以及综合优化路径长度与总风险。该框架在包含静态与动态障碍物的实验场景中进行了评估。结果表明，系统能够实现安全导航、保持轨迹平滑性，并动态适应变化的环境风险。与传统的激光雷达或纯视觉导航方法相比，本方法在运行安全性与自主性方面均有提升，为不确定动态环境下的风险感知自主航行任务提供了有前景的解决方案。