11th International Conference on ICT for Sustainable Development

Authors - Ananya Agrawal, Vedant Agrawalla, Sunil Kumar, Surendra Solanki
Abstract - Investigating ocean depths beyond 5,000 meters presents significant difficulties: pressures surpassing 80 MPa, complete darkness, and intricate landscapes. Conventional ROVs are limited in their ability to adapt in real time and lack high-resolution environmental awareness. This study presents JalX, a next-generation Unmanned Underwater Vehicle (UUV) designed for autonomous missions in deep-sea environments (5–8 km), including search efforts for shipwrecks and lost cities. JalX utilizes ACO-based strategies for adaptive pathfinding, real-time SLAM through the integration of sonar and LiDAR, and AI-driven vision enhanced by GANs for detailed 3D reconstruction. Its hull is made of titanium alloy that withstands extreme underwater pressures, while a hybrid communication system - UWoC, acoustic modems, and satellite links - facilitates uninterrupted data transmission to surface stations. Sophisticated AI manages propulsion and navigation with energy efficiency in mind, allowing for more than 48 hours of autonomous operation in deep-sea conditions. Through system-level design, algorithmic testing, and preliminary field experiments, JalX showcases impressive mapping precision, strong object identification capabilities, and durable communication systems. By merging marine engineering with artificial intelligence and robotics, JalX establishes a new standard for adaptable and intelligent underwater exploration platforms that have the potential to transform oceanographic and archaeological studies.