Deep Reinforcement Learning Based UAV for Indoor Navigation and Exploration in Unknown Environments

Factory planning can increase the productivity of manufacturing significantly, though the process is expensive when it comes to cost and time. In this paper, we propose an Unmanned Aerial Vehicle (UAV) framework that accelerates this process and decreases the costs. The framework consists of a UAV that is equipped with an IMU, a camera and a LiDAR sensor in order to navigate and explore unknown indoor environments. Thus, it is independent of GNSS and solely uses on-board sensors. The acquired data should enable a DRL agent to perform autonomous decision making, applying a reinforcement learning approach. We propose a simulation of this framework including several training and testing environments, that should be used for developing a DRL agent.