EROAS: 3D高效反应性障碍物避障系统：利用2.5D前视声呐的自主水下车辆
EROAS: 3D Efficient Reactive Obstacle Avoidance System for Autonomous Underwater Vehicles using 2.5D Forward-Looking Sonar
AI总结：
本文提出EROAS系统，通过结合2.5D前视声呐与三种模块，实现高效、安全的3D水下障碍物避障，实验验证其在复杂环境中的优势。
摘要：
自主水下车辆（AUVs）在利用声呐、摄像头和基于学习的方法进行障碍检测和路径规划方面已取得显著进展。然而，由于部分可观性、水体浑浊、前视声呐（FLS）的有限视野以及遮挡物遮挡障碍物几何形状，安全且高效的导航在复杂环境中仍具挑战性。为此，我们提出高效反应性避障策略（EROAS），一种轻量级框架，通过在标准2D FLS中添加旋转机制，将其有效转化为成本高效的2.5D声呐。该设计按需提供垂直信息，扩展态势感知能力的同时最小化计算开销。EROAS集成了三个互补模块：首先，声纳轮廓引导方向决策控制（SPD2C）用于快速检测和生成水平和垂直平面的参考命令；其次，空间上下文生成器（SCG），维护过去短期障碍物记忆以缓解部分可观性；最后，时空控制障碍函数（ST-CBF）通过过滤名义参考强制安全约束的前向不变性。这些组件共同使在不确定和复杂的3D水下环境中实现稳健的反应性避障成为可能。仿真和硬件在环（HIL）实验验证了所提EROAS算法的有效性，证明其在轨迹效率、旅行时间和安全性方面优于传统方法，如动态窗口方法（DWA）和人工势场方法（APF）。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
