MUMBAI, India, Aug. 10 -- Intellectual Property India has published a patent application (202641091707 A) filed by Madhankumar C; Dr. B. Phijik - Vignan'S Institute Of Management And Technology For Women; Mrs. J. Banusri - Vignan'S Institute Of Management And Technology For; Mrs. M. Swathi Reddy - Vignan'S Institute Of Management And Technology For Women; Mrs. I. Tavya Sri - Vignan'S Institute Of Management And Technology For; and Mr. V. Anil Kumar - Vignan'S Institute Of Management And Technology on July 29, 2026, for Autonomous Underwater Drone With Bio-Inspired Acoustic Camouflage For Reef Monitoring.
Inventors include Dr. B. Phijik - Vignan'S Institute Of Management And Technology For; Mrs. J. Banusri - Vignan'S Institute Of Management And Technology For; Mrs. M. Swathi Reddy - Vignan'S Institute Of Management And; Mrs. I. Tavya Sri - Vignan'S Institute Of Management And Technology For Women; and Mr. V. Anil Kumar - Vignan'S Institute Of Management And Technology For Women.
The application for the patent was published on July 31, 2026, under issue no. 31/2026.
Abstract: Autonomous Underwater Drone with Bio-Inspired Acoustic Camouflage for Reef Monitoring Abstract Coral reefs are among the most valuable and fragile marine ecosystems, providing biodiversity conservation, coastal protection, and economic benefits through fisheries and tourism. However, conventional underwater monitoring techniques often rely on human divers or noisy autonomous vehicles that disturb marine life, alter natural behavioral patterns, and limit the accuracy of ecological observations. To address these challenges, this invention proposes an Autonomous Underwater Drone with Bio-Inspired Acoustic Camouflage for Reef Monitoring, an intelligent underwater surveillance platform that combines biomimetic acoustic technology, artificial intelligence, autonomous navigation, and multimodal environmental sensing to perform non-invasive, continuous reef monitoring. The proposed underwater drone is designed to emulate the acoustic signatures of naturally occurring marine organisms, enabling it to move through sensitive reef habitats with minimal disturbance to aquatic species. A bio-inspired acoustic camouflage module continuously analyzes ambient underwater soundscapes and dynamically generates adaptive propulsion noise profiles that blend with natural environmental acoustics. This significantly reduces the likelihood of alarming marine organisms and enables long-duration ecological observation without disrupting natural reef behavior. The system integrates multiple sensing technologies, including high-resolution underwater cameras, sonar imaging, hydrophones, water quality sensors, LiDAR (where applicable), inertial navigation systems, pressure sensors, dissolved oxygen sensors, salinity and temperature sensors, and GPS surface synchronization modules. These sensors continuously collect multimodal environmental data regarding coral health, fish populations, reef biodiversity, water chemistry, underwater topography, and environmental changes. Artificial Intelligence algorithms employing deep learning, computer vision, and machine learning automatically identify coral bleaching, invasive species, marine pollution, coral diseases, habitat degradation, illegal fishing activities, and biodiversity variations. Simultaneously, autonomous navigation algorithms utilize simultaneous localization and mapping (SLAM), obstacle avoidance, adaptive path planning, and reinforcement learning to safely navigate complex reef environments while optimizing survey coverage and battery efficiency. An integrated Large Language Model (LLM)-based marine intelligence engine interprets sensor observations, scientific databases, historical monitoring records, and environmental regulations to generate explainable ecological reports, biodiversity assessments, conservation recommendations, and predictive reef health analyses in natural language. Secure cloud-edge communication enables real-time data transmission to marine research centers, environmental agencies, and conservation authorities while supporting collaborative scientific studies through federated learning and encrypted data sharing. Experimental evaluations demonstrate that the proposed bio-inspired underwater drone significantly reduces acoustic disturbance, improves monitoring accuracy, enhances autonomous navigation efficiency, and enables more reliable long-term ecological observations compared with conventional underwater robotic systems. By integrating bio-inspired acoustic camouflage, AI-driven environmental analytics, autonomous underwater navigation, and explainable intelligence within a unified platform, the proposed invention offers an advanced solution for sustainable coral reef conservation, marine biodiversity assessment, climate change monitoring, and intelligent ocean ecosystem management.
Disclaimer: Curated by HT Syndication.
