  Trajectory planning for quadrotors in cluttered environments has been
challenging in recent years. While many trajectory planning frameworks have
been successful, there still exists potential for improvements, particularly in
enhancing the speed of generating efficient trajectories. In this paper, we
present a novel hierarchical trajectory planning framework to reduce
computational time and memory usage called MINER-RRT*, which consists of two
main components. First, we propose a sampling-based path planning method
boosted by neural networks, where the predicted heuristic region accelerates
the convergence of rapidly-exploring random trees. Second, we utilize the
optimal conditions derived from the quadrotor's differential flatness
properties to construct polynomial trajectories that minimize control effort in
multiple stages. Extensive simulation and real-world experimental results
demonstrate that, compared to several state-of-the-art (SOTA) approaches, our
method can generate high-quality trajectories with better performance in 3D
cluttered environments.
