A Survey on Sensor-based Planning and Control for Unmanned Underwater Vehicles

TL;DR

This survey reviews recent sensor-based planning and control strategies for UUVs, emphasizing adaptive local planning, architecture categorization, and controller roles in enhancing underwater navigation autonomy.

Contribution

It provides a taxonomy of planning and control architectures, analyzes adaptive local planning methods, and discusses controller roles in integrated frameworks for UUV navigation.

Findings

Decoupled architectures separate planning and control stages.

Coupled architectures enable tighter feedback and responsiveness.

Model Predictive Control offers path optimization but is computationally demanding.

Abstract

This survey examines recent sensor-based planning and control methods for Unmanned Underwater Vehicles (UUVs). In complex, uncertain underwater environments, UUVs require advanced planning and control strategies for effective navigation. These vehicles face significant challenges including drifting and noisy sensor measurements, absence of Global Navigation Satellite System (GNSS) signals, and low-bandwidth, high-latency underwater acoustic communications. The focus is on reactive local planning layers that adapt to real-time sensor inputs such as SONAR and Inertial Measurement Units (IMU) to improve localization accuracy and autonomy in dynamic ocean conditions, enabling dynamic obstacle avoidance and on-the-fly re-planning. The survey categorizes the existing literature into decoupled and coupled architectures for sensor-based planning and control. The decoupled architecture sequentially addresses planning and control stages, whereas coupled architectures offer tighter feedback loops for more immediate responsiveness. A comparative analysis of coupled planning and control methods reveals that while PID controllers are simple, they lack predictive capability for complex maneuvers. Model Predictive Control (MPC) offers superior path optimization but can be computationally intensive, and invariant-set controllers provide strong safety guarantees at the potential cost of agility in confined environments. Key contributions include a taxonomy of architectures combining planning and control, a focus on adaptive local planning, and an analysis of controller roles in integrated planning frameworks for autonomous navigation of UUVs.