MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641101548 A) filed by Dr. N. Ramaprabha; Sneha Suhasrao Palimkar; Dr. B Naveen Kumar; Dr. Jeevith S H; Dr. V. Santhosh; Dr. Mithilesh Kumar; Dr S M P Qubeb; K Vallarasu; Manicka Senthamarai N; Dr Niharika Parihar; Dr. Pinkey Chouhan; and Dr. Nisha Thakur on August 22, 2026, for Ai Agent-Based Autonomous Waste Collection And Environmental Monitoring System For Smart Cities.
Inventors include Dr. N. Ramaprabha; Sneha Suhasrao Palimkar; Dr. B Naveen Kumar; Dr. Jeevith S H; Dr. V. Santhosh; Dr. Mithilesh Kumar; Dr S M P Qubeb; K Vallarasu; Manicka Senthamarai N; Dr Niharika Parihar; Dr. Pinkey Chouhan; and Dr. Nisha Thakur.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: AI Agent-Based Autonomous Waste Collection and Environmental Monitoring System for Smart Cities is the proposed invention. The proposed invention is an AI agent-based autonomous waste collection and environmental monitoring system for smart cities using a hierarchical multi-agentic AI framework integrated with IoT, edge computing, multimodal sensing, reinforcement learning, and autonomous collection vehicles. Smart bins and environmental sensors continuously acquire waste- level, weight, temperature, gas, air-quality, location, and image data. Specialized AI agents perform waste perception, accumulation prediction, collection prioritization, route optimization, environmental monitoring, anomaly detection, and vehicle coordination. A supervisory orchestration agent integrates these outputs and autonomously generates collection tasks based on overflow probability, vehicle capacity, traffic, energy consumption, distance, and environmental conditions. Reinforcement learning continuously improves task allocation and routing using operational feedback. Environmental agents detect pollution hotspots, illegal dumping, hazardous conditions, and abnormal sensor readings and initiate appropriate responses. The proposed closed-loop sense–reason–plan–act–learn architecture enables adaptive waste collection, efficient resource utilization, reduced unnecessary travel, improved overflow prevention, and real-time environmental management in smart cities.
Disclaimer: Curated by HT Syndication.
