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This survey analyzes sensor-based planning and control methods for Unmanned Underwater Vehicles (UUVs) operating in complex, uncertain underwater environments. It categorizes existing architectures into decoupled and coupled approaches, highlighting the trade-offs between reactive control and computational complexity. The analysis reveals that Model Predictive Control (MPC) offers superior path optimization, while invariant-set controllers provide strong safety guarantees, informing the selection of appropriate control strategies for autonomous UUV navigation.
Choosing the right UUV controller is a nuanced game of balancing path optimization (MPC), safety guarantees (invariant-set), and computational cost.
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.