Autonomous Underwater Vehicles (AUVs) have advanced significantly in obstacle detection and path planning through sonar, cameras, and learning-based methods. However, safe and efficient navigation in cluttered environments remains challenging due to partial observability, turbidity, the limited field-of-view of forward-looking sonar (FLS), and occlusions that obscure obstacle geometry. To address these issues, we propose the Efficient Reactive Obstacle Avoidance Strategy (EROAS), a lightweight framework that augments a standard 2D FLS with a pivoting mechanism, effectively transforming it into a cost-efficient \emph{2.5D sonar}. This design provides vertical information on demand, extending situational awareness while minimizing computational overhead. EROAS integrates three complementary modules: first, Sonar Profile-guided Directional Decision Control (SPD2C) for rapid gap detection and generation of reference commands in both horizontal and vertical planes. Secondly, the Spatial Context Generator (SCG), which maintains a short-term obstacle memory of the past to mitigate partial observability, and finally, a Spatio-Temporal Control Barrier Function (ST-CBF) that enforces forward-invariance of safety constraints by filtering nominal references. Together, these components enable robust, reactive avoidance of obstacles in uncertain and cluttered 3D underwater settings. Simulation and hardware-in-the-loop (HIL) experiments validate the efficacy of the proposed EROAS algorithm, demonstrating improved trajectory efficiency, reduced travel time, and enhanced safety compared to conventional methods such as the Dynamic Window Approach (DWA) and Artificial Potential Fields (APF). https://github.com/AIRLabIISc/EROAS

翻译：自主水下航行器（AUV）已通过声纳、摄像头及基于学习的方法在障碍物检测与路径规划方面取得显著进展。然而，在杂乱环境中实现安全高效的导航仍面临挑战，主要归因于部分可观测性、水体浑浊、前视声纳（FLS）有限的视场角以及遮挡物掩盖障碍物几何形状等问题。为解决上述难题，我们提出了高效反应式障碍物规避策略（EROAS），这是一个轻量级框架，通过为标准的2D前视声纳添加旋转机制，将其转化为成本效益高的“2.5D声纳”。该设计可按需提供垂直方向信息，在提升环境感知能力的同时最大限度地降低计算开销。EROAS集成了三个互补模块：首先，基于声纳剖面引导的定向决策控制模块（SPD2C），用于快速检测间隙并在水平与垂直平面生成参考指令；其次，空间上下文生成器（SCG），通过维护短期障碍物记忆缓解部分可观测性问题；最后，时空控制障碍函数（ST-CBF），通过滤波标称参考信号确保安全约束的前向不变性。这些组件共同实现了在不确定且杂乱的三维水下环境中对障碍物的鲁棒反应式规避。仿真与硬件在环（HIL）实验验证了所提出的EROAS算法的有效性，结果表明：与动态窗口法（DWA）及人工势场法（APF）等传统方法相比，该算法在轨迹效率、航行时间及安全性方面均有显著提升。https://github.com/AIRLabIISc/EROAS