Problem
Research questions and friction points this paper is trying to address.
Unmanned Underwater Vehicles
sensor-based planning
autonomous navigation
underwater environment
dynamic obstacle avoidance
A Survey on Sensor-based Planning and Control for Unmanned Underwater Vehicles
🤖 AI Summary
This study addresses the navigation and control challenges faced by unmanned underwater vehicles (UUVs) in complex underwater environments characterized by GNSS denial, sensor noise, and limited communication. It provides a systematic review and classification of local reactive planning and control methods that leverage real-time sensing data from sonar, inertial measurement units (IMUs), and other onboard sensors. The work innovatively proposes an architectural taxonomy that distinguishes between decoupled and coupled planning–control paradigms, emphasizing adaptive local replanning mechanisms. By integrating strategies such as PID control, model predictive control (MPC), and invariant-set-based control, the paper offers a thorough analysis of the trade-offs among path optimality, computational cost, safety, and maneuverability, thereby establishing a theoretical foundation and practical design guidance for enhancing UUV autonomy.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
sensor-based planning
coupled architecture
adaptive local planning
Model Predictive Control
autonomous underwater navigation
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