Autonomous Underwater Robotics for Deep-Sea Monitoring Using Advanced Artificial Intelligence Techniques

Abstract

Deep sea ecosystems remain among the least explored environments due to extreme conditions such as low illumination, high pressure, and limited accessibility. To address these challenges, this research proposes an intelligent robotic framework that integrates autonomous underwater robotics with advanced artificial intelligence for deep-sea region analysis and sea-living community monitoring. The system employs an autonomous underwater robot equipped with multimodal sensors to acquire visual, acoustic, and environmental data from deep-sea environments. A robust preprocessing pipeline incorporating median, Gaussian, bilateral, and Kalman filtering techniques is applied to enhance data quality and suppress noise inherent in underwater sensing.

The enhanced data are analyzed using deep learning models, including convolutional neural networks for organism detection and classification, U-Net-based architectures for semantic segmentation, Transformer models for contextual feature learning, and long short-term memory networks for temporal pattern analysis. The proposed framework is evaluated using annotated underwater image datasets, acoustic patterns, bathymetric maps, and environmental measurements collected from diverse deep sea regions. Experimental results demonstrate that the proposed approach achieves a detection accuracy of 94.2% and a segmentation Intersection over Union of 0.91, outperforming conventional deep learning and feature-based methods. In addition, the framework maintains computational efficiency with an average inference time of 0.35 seconds per image, enabling near real-time deployment. The results confirm that the integration of intelligent robotics, advanced preprocessing, and deep learning provides a reliable and efficient solution for deep sea exploration and marine ecosystem analysis. The proposed framework offers significant potential for long-term ecological monitoring, biodiversity assessment, and autonomous deep-sea research applications.