Learning Unmanned Aerial Vehicle Control for Autonomous Target Following

While deep reinforcement learning (RL) methods have achieved unprecedented successes in a range of challenging problems, their applicability has been mainly limited to simulation or game domains due to the high sample complexity of the trial-and-error learning process. However, real-world robotic applications often need a data-efficient learning process with safety-critical constraints. In this paper, we consider the challenging problem of learning unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire a strategy that combines perception and control, we represent the policy by a convolutional neural network. We develop a hierarchical approach that combines a model-free policy gradient method with a conventional feedback proportional-integral-derivative (PID) controller to enable stable learning without catastrophic failure. The neural network is trained by a combination of supervised learning from raw images and reinforcement learning from games of self-play. We show that the proposed approach can learn a target following policy in a simulator efficiently and the learned behavior can be successfully transferred to the DJI quadrotor platform for real-world UAV control

Control of a drone with body gestures

Drones are becoming more popular within military applications and civil aviation by hobbyists and business. Achieving a natural Human-Drone Interaction (HDI) would enable unskilled drone pilots to take part in the flying of these devices and more generally easy the use of drones. The research within this paper focuses on the design and development of a Natural User Interface (NUI) allowing a user to pilot a drone with body gestures. A Microsoft Kinect was used to capture the user's body information which was processed by a motion recognition algorithm and converted into commands for the drone. The implementation of a Graphical User Interface (GUI) gives feedback to the user. Visual feedback from the drone's onboard camera is provided on a screen and an interactive menu controlled by body gestures and allowing the choice of functionalities such as photo and video capture or take-off and landing has been implemented. This research resulted in an efficient and functional system, more instinctive, natural, immersive and fun than piloting using a physical controller, including innovative aspects such as the implementation of additional functionalities to the drone's piloting and control of the flight speed