Hannan Ejaz Keen

"Autonomous Navigation and Mapping in Disastrous Environment using Unmanned Aerial Vehicle"

Unmanned aerial vehicles (UAVs) have been extensively encouraged to use in industrial, civil and defense applications. During natural disasters such as flood, earthquakes and wildfires, UAVs are used to monitor situations in real-time. Autonomous UAVs for critical tasks in disastrous environment, require precise navigation and mapping of the environment. Numerous Deep Reinforcement Learning (DRL) based navigation approaches and vision or laser based reactive techniques have been proposed in literature. However these approaches have their own shortcomings. Our current robotic system (Octocopter) has been upgraded to provide all necessary sensor data. This research proposes the work towards efficient navigation through disastrous environment by optimizing the current DRL techniques and maps it robustly to conclude critical information of the calamity.