Intellectual Property India Publishes Patent Application for Adaptive Deep Reinforcement Learning System For Real-Time Navigation In Complex Autonomous Environments

MUMBAI, India, Aug. 10 -- Intellectual Property India has published a patent application (202641091693 A) filed by Ms. Kochumol Abraham; Dr. Binda M B; Mr. Harendra Bhati; Mr. Maligireddy Jalendar Reddy; Dr. Y. M. Mahaboobjohn; Mr. G D Vignesh; Preethi Santhosam P; Dr. B. Thanikaivel; Dr. Jotesh Gupta; Pragati Mishra; and Prof. Dr. Harikumar Pallathadka on July 28, 2026, for Adaptive Deep Reinforcement Learning System For Real-Time Navigation In Complex Autonomous Environments.

Inventors include Ms. Kochumol Abraham; Dr. Binda M B; Mr. Harendra Bhati; Mr. Maligireddy Jalendar Reddy; Dr. Y. M. Mahaboobjohn; Mr. G D Vignesh; Preethi Santhosam P; Dr. B. Thanikaivel; Dr. Jotesh Gupta; Pragati Mishra; and Prof. Dr. Harikumar Pallathadka.

The application for the patent was published on July 31, 2026, under issue no. 31/2026.

Abstract: The present invention relates to an adaptive deep reinforcement learning system for real-time navigation in complex autonomous environments. The system comprises a sensor data acquisition module configured to receive multi-modal environmental information, an environmental state representation engine for generating dynamic contextual models, an adaptive deep reinforcement learning engine for optimising navigation policies, a reward optimisation module for evaluating navigation objectives, a trajectory prediction module for proactive path planning and collision avoidance, a real-time navigation controller for executing autonomous navigation actions, and a continuous learning module for dynamically refining navigation strategies during deployment. The disclosed system enables intelligent and context-aware decision-making by continuously adapting to changing environmental conditions without complete model retraining. The invention improves navigation accuracy, operational safety, scalability, and computational efficiency across heterogeneous autonomous platforms including autonomous vehicles, robotic systems, unmanned aerial vehicles, marine systems, and industrial automation environments.

Disclaimer: Curated by HT Syndication.