EROAS: 3D Efficient Reactive Obstacle Avoidance System for Autonomous Underwater Vehicles using 2.5D Forward-Looking Sonar

TL;DR

EROAS is a lightweight 3D obstacle avoidance system for AUVs that enhances situational awareness using a 2.5D sonar and reactive modules, validated through simulations and HIL tests.

Contribution

The paper introduces EROAS, a novel 2.5D sonar-based reactive obstacle avoidance framework with modules for gap detection, obstacle memory, and safety filtering, improving navigation in cluttered underwater environments.

Findings

EROAS improves trajectory efficiency and safety in simulations.

The system reduces travel time compared to traditional methods.

Validation through hardware-in-the-loop experiments confirms robustness.

Abstract

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