MUMBAI, India, Feb. 6 -- Intellectual Property India has published a patent application (202541085109 A) filed by Saveetha Engineering College, Chennai, Tamil Nadu, on Sept. 8, 2025, for 'neuromorphic ai-enabled drone for real-time adaptive decision making in dynamic environments.'
Inventor(s) include Dr. K. Gokulkannan.
The application for the patent was published on Feb. 6, under issue no. 06/2026.
According to the abstract released by the Intellectual Property India: "The present invention discloses a Neuromorphic AI-Enabled Drone System that integrates brain-inspired computational architectures with autonomous aerial platforms to achieve real-time adaptive decision making in dynamic and unpredictable environments. Conventional drones rely on centralized computation and pre-programmed algorithms, which limit adaptability, introduce latency, and demand significant energy resources. The proposed invention addresses these limitations by embedding neuromorphic computing engines based on spiking neural networks (SNNs) and event-driven computation, enabling efficient parallel processing of sensory inputs with low latency and minimal energy consumption. The system comprises four principal modules: (i) a neuromorphic computing engine that processes asynchronous sensory events such as visual, auditory, and spatial inputs in real time, (ii) a self-learning adaptive control framework that employs reinforcement learning and online training to continuously refine navigation, obstacle avoidance, and task execution, (iii) an adaptive sensor fusion module that integrates data from multiple onboard sensors including cameras, LiDAR, GPS, and inertial units, ensuring robust situational awareness, and (iv) a fault- tolerant resilience layer designed to maintain operational continuity even in the presence of sensor malfunctions, communication loss, or external disturbances. By mimicking biological intelligence, the invention allows drones to autonomously learn and adapt to environmental changes such as weather fluctuations, terrain variability, unexpected obstacles, or adversarial interference without reliance on cloud-based computation. The energy-efficient architecture further extends operational endurance, making the system particularly suitable for mission-critical scenarios where power and computational resources are constrained. Applications of this invention are broad and include search and rescue missions where drones must adaptively navigate debris-filled areas, disaster relief operations where conditions evolve rapidly, defense and reconnaissance missions in communication-denied environments, precision agriculture requiring dynamic crop monitoring and spraying, urban traffic analysis, and environmental hazard mapping for real-time ecosystem assessment. By combining neuromorphic computation with UAV autonomy, the proposed invention establishes a novel biologically inspired paradigm for intelligent aerial robotics, providing superior adaptability, scalability, and resilience over conventional AI-driven drone systems. This invention represents a transformative advancement in autonomous drones, enabling next-generation UAVs to operate effectively in diverse, uncertain, and resource-limited environments."
Disclaimer: Curated by HT Syndication.
