  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. Our method constructs
collision-free paths using heuristic search and visibility graphs, which are
then refined by an RL policy to generate low-level motion commands. We validate
our approach in simulated indoor environments, demonstrating better performance
than traditional methods in terms of time span.
