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

TL;DR

This paper introduces EDEN, a dual-layer exploration planning approach that enables fast, efficient, and real-time autonomous UAV exploration in large 3D environments by combining approximate long-term routing with greedy local exploration.

Contribution

The paper presents a novel dual-layer planning method that significantly improves exploration speed and efficiency in large environments, with real-time capabilities and validated through simulations and real-world tests.

Findings

Outperforms state-of-the-art methods in exploration efficiency

Reduces computational cost and trajectory deceleration

Validated effectiveness through real-world experiments

Abstract

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.