Automatic Drone Navigation in Realistic 3D Landscapes using Deep Reinforcement Learning

Abstract

We present a study where a drone navigates through diverse 3D obstacles by finding a 3D path and reaches the goal using deep reinforcement learning (RL) in a 3D realistic landscape. The drone has two inputs: first RGB provides a first person view of the landscape and secondly depth map gives it 3D information of the environment. For training the drone for automatic navigation, deep reinforcement learning is extensively used. For the same task, human pilot navigates through the obstacles with a radio controller (RC) using a hardware-in-the-loop setup. Racing performance between human and several deep RL algorithms such as Deep Q-Network (DQN), Double DQN, Dueling DQN and Double Dueling DQN (DD-DQN) are evaluated. Results suggest that DD-DQN outperforms other algorithms and, for the racing between humans and algorithms, DD-DQN performs better than a novice and yet an expert or intermediate-level pilot outperforms any other algorithms. The present study demonstrates that time and resource for training a drone can be saved using a realistic and yet controllable platform.