EDEN: 大規模3次元環境における高速UAV自律探査のための効率的二層探査計画
EDEN: Efficient Dual-Layer Exploration Planning for Fast UAV Autonomous Exploration in Large 3-D Environments
大規模3次元環境でのUAV自律探査を高速化するため、長期的な領域経路計画と曲率ペナルティを考慮した局所的な視点選択を組み合わせた二層探査計画手法を提案し、シミュレーションと実機実験で有効性を示した。
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著者: Qianli Dong, Xuebo Zhang, Shiyong Zhang, Ziyu Wang, Zhe Ma, Haobo Xi
分類: cs.RO
原文アブストラクト
Efficient autonomous exploration in large-scale environments remains challenging due to the high planning computational cost and low-speed maneuvers. In this paper, we propose a fast and computationally efficient dual-layer exploration planning method. The insight of our dual-layer method is efficiently finding an acceptable long-term region routing and greedily exploring the target in the region of the first routing area with high speed. Specifically, the proposed method finds the long-term area routing through an approximate algorithm to ensure real-time planning in large-scale environments. Then, the viewpoint in the first routing region with the lowest curvature-penalized cost, which can effectively reduce decelerations caused by sharp turn motions, will be chosen as the next exploration target. To further speed up the exploration, we adopt an aggressive and safe exploration-oriented trajectory to enhance exploration continuity. The proposed method is compared to state-of-the-art methods in challenging simulation environments. The results show that the proposed method outperforms other methods in terms of exploration efficiency, computational cost, and trajectory speed. We also conduct real-world experiments to validate the effectiveness of the proposed method. The code will be open-sourced.
