AI, Automation, Robotics & Drone Technology
City-Scale AI-Driven Urban Risk Intelligence System
Modern urban environments face compound and cascading risks such as flooding, heatwaves, and air pollution due to climate change, rapid urbanization, and aging infrastructure. Participants are required to design an AI-driven early warning and decision-support system that integrates real-time IoT data, satellite imagery, historical climate data, and urban infrastructure information.
Explainable Multi-Modal AI Framework for Medical Diagnosis
Healthcare AI solutions often fail in resource-constrained hospitals due to data silos, lack of interpretability, and fairness concerns. There is a need for a clinically reliable and explainable AI system that can integrate diverse medical data while adhering to ethical and regulatory requirements.
Multi-Agent Reinforcement Learning for Traffic Intelligence
Urban traffic systems are dynamic, adversarial, and multi-agent. Traditional traffic signal control systems fail to adapt to real-time changes, leading to congestion and delayed emergency response. Participants must develop a multi-agent reinforcement learning (MARL) system to optimize traffic signals, predict accidents, and prioritize emergency vehicles.
Cross-Platform Misinformation Intelligence System
Misinformation spreads rapidly across platforms using text, images, videos, and coordinated networks. Manual moderation is insufficient to detect such campaigns. Participants must develop a cross-platform misinformation detection system capable of identifying fake news, deepfakes, and coordinated disinformation campaigns.
Autonomous Infrastructure Monitoring via Drones
Manual inspection of infrastructure is costly, slow, and unsafe. Cities require autonomous drone-based inspection systems for early detection of structural anomalies. Participants must design an autonomous drone system for real-time infrastructure monitoring, anomaly detection, and reporting.
AI-Enabled Disaster Response and Search-and-Rescue Robotics
Natural and man-made disasters such as earthquakes, floods, fires, and industrial accidents require rapid and coordinated search-and-rescue (SAR) operations. Human responders face high risk, limited visibility, and time-critical constraints. Participants must design an AI-powered robotic and drone-assisted SAR system that autonomously explores disaster zones, detects survivors, and supports rescue planning.
Intelligent Robotic Automation for Smart Warehousing
Modern warehouses require high efficiency, accuracy, and adaptability to dynamic demand. Traditional automation systems lack intelligence, flexibility, and real-time optimization. Participants must develop an AI-driven robotic warehouse automation system for inventory handling, order picking, and logistics optimization.
AI-Driven Precision Agriculture using Autonomous Drones and Robots
Agriculture faces challenges such as water scarcity, crop diseases, and inefficient resource usage. Precision agriculture using AI and robotics can significantly improve productivity and sustainability. Participants must design an autonomous system using drones and ground robots for crop monitoring, disease detection, and targeted intervention.
Autonomous Surveillance and Threat Detection System
Critical infrastructure such as airports, borders, power plants, and campuses require continuous surveillance to detect intrusions and anomalous activities. Participants must develop an AI-enabled autonomous surveillance system using drones, cameras, and sensors for real-time threat detection and response.
Human-Robot Collaboration System for Industrial Safety
In industrial environments, close collaboration between humans and robots increases productivity but introduces safety risks if not properly managed. Participants must design an AI-powered human–robot collaboration system that ensures safety, adaptability, and efficiency in smart manufacturing setups.