A Reinforcement Learning Based Motion Planner for Quadrotor Autonomous Flight in Dense Environment

Abstract-- Quadrotor motion planning is critical for autonomous flight in complex environments, such as rescue operations. Traditional methods often employ trajectory generation optimization and passive time allocation strategies, which can limit the exploitation of the quadrotor's dynamic capabilities and introduce delays and inaccuracies. To address these challenges, we propose a novel motion planning framework that integrates visibility path searching and reinforcement learning (RL) motion generation. Figure 1: Illustration of the proposed method. Finally, an RL policy is used to generate control commands based on the quadrotor's I. Quadrotors are extensively used in a variety of applications, including rescue operations, fire and electricity inspection, and package delivery.