用2.5D声呐实现水下无人艇高效避障，兼顾安全与实时性。
EROAS: 3D Efficient Reactive Obstacle Avoidance System for Autonomous Underwater Vehicles using 2.5D Forward-Looking Sonar
- 将2D前视声呐加旋转机构转为2.5D，按需获取垂直信息
- 仿真与硬件在环测试中比DWA和APF快18%且更安全
- 适合资源受限的水下机器人实时避障应用
自主水下航行器（AUV）在障碍物检测与路径规划方面已通过声呐、摄像头及学习方法取得显著进展。然而，在复杂环境中实现安全高效的导航仍面临挑战，主要源于观测不全、浑浊水体、前视声呐（FLS）视场有限及遮挡导致的障碍物几何信息缺失。为此，我们提出轻量级的高效反应式避障策略（EROAS），通过在标准2D FLS上增加旋转机构，实现按需获取垂直信息，低成本构建出“2.5D声呐”，增强态势感知能力的同时最小化计算开销。EROAS集成三个互补模块：首先，基于声呐剖面的方向决策控制（SPD2C）可快速检测空隙并生成水平与垂直方向的参考指令；其次，空间上下文生成器（SCG）通过保持短时障碍物记忆以缓解观测不全问题；最后，时空控制屏障函数（ST-CBF）通过过滤原始参考指令，确保安全约束的前向不变性。三者协同使系统能在不确定且复杂的3D水下环境中实现鲁棒的反应式避障。仿真与硬件在环（HIL）实验验证了该算法的有效性，相较于动态窗口法（DWA）和人工势场法（APF），EROAS在轨迹效率、旅行时间与安全性方面均有显著提升。
原文摘要 · Abstract (English)
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
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