Flight Evolution: Decoding Autonomous UAV Navigation—Fundamentals, Taxonomy, and Challenges

Abstract

Due to the adaptability and effectiveness of autonomous unmanned aerial vehicles (UAVs) in completing challenging tasks, research on UAVs has increased quickly during the past few years. An autonomous UAV refers to drone navigation in an unknown environment with minimal human interaction. However, when used in a dynamic environment, UAVs confront numerous difficulties including scene mapping and localization, object recognition and avoidance, path planning, emergency landing, and so forth. Real-time UAVs demand quick responses to situations; as a result, this is a crucial feature that requires further research. This article presents different novel taxonomies to briefly explain UAVs and the communication architecture utilized during the communication of UAVs with ground stations. Popular databases for UAVs, and the fundamentals of autonomous navigation including the latest ongoing object detection and avoidance methods, path planning techniques, and trajectory mechanisms are also explained. Later, we cover the benchmark dataset available and the different kinds of simulators used in UAVs. Furthermore, several research challenges are covered. From the literature, it has been found that algorithms based on deep reinforcement learning (DRL) are employed more frequently than other intelligence algorithms in the field of UAV navigation. To the best of our knowledge, this is the first article that covers different aspects related to UAV navigation.