Trajectory Planning-Based Reinforcement Learning for 3-D Indoor Navigation of Autonomous Aerial Vehicle

The development of indoor navigation and positioning technology autonomous aerial vehicles, or AAVs, are employed extensively in many different industries due to their ease of operation and outstanding mobility. Drones can decrease responder injuries, increase mission effectiveness, and conduct search operations in locations that rescue crews are unable to access when utilized for indoor search and rescue during a disaster. This paper’s goal is to use a reinforcement learning algorithm to suggest a track planning technique for AAVs in indoor post-disaster scenarios. The difficulty of finding a safe path for AAVs in crowded indoor areas that present a significant risk to flight safety is the focus of this research. To address the difficulties caused by the intricate interior environment and the sluggish convergence of current reinforcement learning algorithms, an ensemble strategy based on reinforcement learning has been put forth. By using the coordinate connection between the beginning point and the endpoint to identify the primary barrier and the nodes surrounding it, this strategy seeks to minimize the search for superfluous nodes. The enhanced 3D navigation trajectory planning technique lowered the number of space search nodes by 55.49% and shortened the convergence time by 98.57%, according to simulation and testing of the optimization process in a three-dimensional grid diagram.