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

2026-04-06

AI Summary

Autonomous underwater vehicles (AUVs) operating in unknown environments often suffer from poor visibility, limited communication, and ocean current disturbances, leading to inaccurate localization, challenging obstacle avoidance, and unstable control. This work proposes an integrated planning-and-control framework that generates closed-loop trajectories online via a prescribed feedback controller, effectively fusing real-time sensor data to enable agile navigation and efficient collision avoidance. By embedding motion planning directly within the control loop, the approach substantially reduces the computational burden of online optimization. Simulations in ROS-Gazebo and real-world experiments with the RexRov AUV demonstrate that, compared to conventional PID-based trajectory tracking, the proposed method significantly enhances obstacle avoidance performance and effectively mitigates localization errors when entering target communication ranges.

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.

Problem

• autonomous underwater vehicles
• unknown environments
• obstacle avoidance
• sensor uncertainty
• real-time navigation

Innovation

• integrated planning and control
• real-time sensor feedback
• closed-loop trajectory generation
• underwater obstacle avoidance
• computational efficiency