eXplore-D

Abstract

One of the most desirable characteristics of UAVs is autonomous navigation, which aids in a variety of applications such as search and rescue missions, delivery, surveillance, and so on. UAVs are equipped with various sensors such as stereo cameras and LIDAR to gather information about their surroundings for navigation purposes. UAVs fly safely by using a 3D map and an obstacle-free path, but this requires more on-board computational resources and power. Because UAVs lack these resources due to their small size and limited battery capacity, using path planning algorithms is not an efficient solution. To address this issue, we used deep reinforcement learning to learn the action policy on its own and take actions based on it in order to navigate successfully without the use of a map. As indoor environments are more dynamic in nature that's why we have created multiple indoor simulated environments in the Unreal Engine framework in which we have trained and tested our drone.