Instantaneous Planning, Control and Safety for Navigation in Unknown Underwater Spaces

TL;DR

This paper presents an integrated real-time planning and control framework for autonomous underwater vehicles to navigate unknown, complex underwater environments safely and efficiently using sensor data and feedback controllers.

Contribution

It introduces a novel approach that reduces online computation and improves safety by dynamically planning trajectories based on real-time sensor feedback.

Findings

Validated through ROS Gazebo simulations on RexRov AUV.

Demonstrated improved obstacle avoidance and maneuverability.

Quantified localization errors during transition to communication range.

Abstract

Navigating autonomous underwater vehicles (AUVs) in unknown environments is significantly challenging due to poor visibility, weak signal transmission, and dynamic water currents. These factors pose challenges in accurate global localization, reliable communication, and obstacle avoidance. Local sensing provides critical real time environmental data to enable online decision making. However, the inherent noise in underwater sensor measurements introduces uncertainty, complicating planning and control. To address these challenges, we propose an integrated planning and control framework that leverages real time sensor data to dynamically induce closed loop AUV trajectories, ensuring robust obstacle avoidance and enhanced maneuverability in tight spaces. By planning motion based on pre designed feedback controllers, the approach reduces the computational complexity needed for carrying out online optimizations and enhances operational safety in complex underwater spaces. The proposed method is validated through ROS Gazebo simulations on the RexRov AUV, demonstrating its efficacy. Its performance is evaluated by comparison against PID based tracking methods, and quantifying localization errors in dead reckoning as the AUV transitions into the target communication range.