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This paper introduces an integrated planning and control framework for autonomous underwater vehicle (AUV) navigation in unknown environments, addressing challenges like poor visibility and sensor noise. The framework uses real-time sensor data to generate closed-loop AUV trajectories based on pre-designed feedback controllers, reducing computational complexity and enhancing safety. Simulations on the RexRov AUV in ROS Gazebo demonstrate the framework's efficacy in obstacle avoidance and maneuverability compared to PID-based tracking.
Pre-designed feedback controllers can enable real-time AUV navigation in complex underwater environments, outperforming traditional PID methods while minimizing computational cost.
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.