Navigation and Control Strategies for Remotely Operated Underwater Vehicles in Unknown Waters

Abstract

The desire to explore new marine environments has driven the development of autonomous underwater vehicles. The goals have been to expand scientific research, assess the economic potential, and gather information for military actions. All these areas require accurate underwater ecological understanding. Successful operations in managing these complicated vehicles require overcoming significant hurdles in the diverse and unpredictable maritime environment. This paper studies field-used acoustic locating devices, inertial navigation, and vision-based methods. In this case, each method's merits and downsides are compared. The Deep Reinforcement Learning (DRL) and sensor fusion approaches advance underwater real-time navigation. These technologies enhance underwater and vehicle movement control. Advanced nonlinear model predictive control and adaptive machine learning have been explored for environmental disturbances and nonlinear dynamics to improve vehicle stability and efficiency. Automation has improved, yet precision tasks still require humans. Besides, enhanced interfaces that reduce operator workload help. This paper’s qualitative investigation evaluated these solutions' technological performance indicators, environmental adaptability, and operational practicality. It accentuates hybrid systems that seamlessly integrate many navigation and control approaches. Thus, undersea research, resource management, and ecological monitoring will become more sustainable and effective.