REINFORCEMENT LEARNING FOR AUTONOMOUS UAV NAVIGATION: INTELLIGENT DECISION-MAKING AND ADAPTIVE FLIGHT STRATEGIES

Abstract

It presented the integration of reinforcement learning into the drone autonomous navigation, which achieved the impressive improvement in the aircraft operation in a dynamic, complex environment. In this research paper we elaborate the latest technological breaks in this area, and we describe the use of Deep Reinforcement Learning (DRL), i.e., the DRL, which is a combination of neural networks and RL capable of handling high-dimensional sensory data for more sophisticated decision making. Computer vision relies on the analysis of an image to extract information from vision inputs that simplify tasks particularly related to obstacle avoidance or target tracking performed without the aid of GPS. To ensure stable and efficient training process to run in the real time, advanced algorithms were used such as Proximal Policy Optimization (PPO). Drones can construct and update maps of unknown areas as they explore uncharted areas, and find their location with the help of Simultaneous Localization and Mapping (SLAM) techniques. Multi Agent Reinforcement Learning (MARL) allows multiple of the drones to work together as they share information in order to optimize their collective navigation strategies. Moreover, transfer learning techniques have been used to transfer the knowledge gained in the simulated environment to real world and reduce the training time and enhance adaptability. Together, these technological advancements enable the establishment of strong, efficient and smart autonomous drone navigation systems which can perform the complicated jobs in different operation environments.