MINER-RRT*: 3次元混雑環境における階層的で高速な軌道計画フレームワーク
MINER-RRT*: A Hierarchical and Fast Trajectory Planning Framework in 3D Cluttered Environments
ニューラルネットでRRT*の探索を加速し、微分平坦性に基づく多段多項式軌道生成を組み合わせることで、3次元混雑環境で高速かつ高品質なドローン軌道計画を実現した。
著者: Pengyu Wang, Jiawei Tang, Hin Wang Lin, Fan Zhang, Chaoqun Wang, Jiankun Wang, Ling Shi, Max Q. -H. Meng
分類: cs.RO
原文アブストラクト
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.
