Deep Learning Techniques for Autonomous Navigation of Underwater Robots

Underwater exploration, environmental monitoring, and infrastructure inspection are just few of the many applications where autonomous navigation of underwater robots is a tough and crucial task. The undersea world is complicated and ever-changing, making conventional navigation systems ineffective. When it comes to autonomous and effective underwater robot navigation, deep learning, and more especially Convolutional Neural Networks (CNNs), has emerged as a potent technique. This study reviews the use of convolutional neural networks (CNNs) in the context of underwater robot navigation in great detail. The major difficulties of underwater navigation are covered, including vision issues, changing illumination, and the existence of objects. The article goes on to investigate how convolutional neural networks (CNNs) might help solve these problems and boost navigational accuracy. CNNs' capacity to extract useful characteristics from unprocessed sensor data like sonar, LiDAR, and camera pictures is a major benefit. These capabilities improve underwater robots' perception, allowing them to avoid obstacles, identify objects, and map their environments. Because of their flexibility in responding to novel situations, CNN s are well-suited for use in the real world. Additionally, this research explores the methods of instruction and transfer learning that are most applicable to the field of underwater navigation. Reduce the burden of collecting massive amounts of data in harsh underwater settings by applying what has been learned in simulations to real-world circumstances.We also investigate how to combine CNNs with other sensor fusion methods like Simultaneous Localization and Mapping (SLAM) algorithms. These synergies boost underwater robots' overall navigation ability, allowing them to safely and independently traverse unfamiliar and sometimes dangerous terrain. This study also describes recent developments and case studies in which CNN s played a critical role in accomplishing autonomous navigation objectives. It demonstrates how deep learning approaches may enable adaptive and intelligent decision-making processes, which might completely transform the field of underwater robots. In conclusion, this article demonstrates how crucial CNN s are to developing the technology of underwater robots that can navigate autonomously. Using CNN-based techniques, it shows the potential for enhancing the efficacy, safety, and autonomy of underwater robotic systems, as well as the problems and possibilities involved with implementing deep learning in such settings. This paper's findings aid continuing attempts to design smarter, more competent, and more versatile underwater robots.