Project title: Lander.AI: DRL-based Autonomous Drone Landing on Moving 3D Surface in the Presence of Aerodynamic Disturbances

Description:
Autonomous drone landing on dynamic platforms presents formidable challenges due to unpredictable velocities and aerodynamic disturbances. This project focuses on an advanced Deep Reinforcement Learning (DRL) agent, Lander.AI, designed to navigate and land on 3D moving platforms in the presence of aerodynamic disturbance and sudden velocity changes, thereby enhancing drone autonomy and safety. Lander.AI is trained in the gym-pybullet-drone simulation, an environment that mirrors real-world complexities, including wind disturbance, to ensure the agent’s robustness and adaptability. The agent’s capabilities will be empirically validated with Crazyflie 2.1 drones across various test scenarios, encompassing both simulated environments and real-world conditions. This research will not only advance the drone landing technologies, essential for inspection and emergency applications, but also highlights the potential of DRL in addressing intricate aerodynamic challenges.

Candidates requirements:
➔ basic programming skills in Python or ROS / Gazebo

Supervisor: Associate Professor Dzmitry Tsetserukou

Internship duration: 1-2 months

The start date of the internship: July 12

End day: August 31

Contact person: Dzmitry Tsetserukou d.tsetserukou@skoltech.ru