EDEN: Efficient Dual-Layer Exploration Planning for Fast UAV Autonomous Exploration in Large 3-D Environments

Efficient autonomous exploration in large-scale environments remains challenging due to high planning computational cost and low-speed maneuvers. In this article, we propose an efficient dual-layer exploration planning method. The insight of our dual-layer planning method is efficiently finding an acceptable long-term region routing and greedily exploring the target in the first region of routing at high speed. Specifically, we propose a long-term region routing approximate algorithm, called “exploration-oriented heuristic double-tree algorithm”, to ensure real-time planning in large-scale environments. Then, the viewpoint in the first routing region with the highest curvature-penalized score, 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 propose an aggressive and safe exploration-oriented trajectory planning approach to enhance exploration continuity and speed. The proposed method is compared with 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 https://github.com/NKU-MobFly-Robotics/EDEN.