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

2025-06-05

AI Summary

To address the high computational cost of path planning and low flight speed—two key bottlenecks in autonomous UAV exploration within large-scale 3D environments—this paper proposes an efficient two-layer exploration planning framework. At the upper layer, a lightweight global topological path planner is realized via an approximate region routing algorithm; at the lower layer, a curvature-penalized viewpoint optimization scheme and an exploration-driven aggressive trajectory generation mechanism jointly enhance motion continuity and exploratory proactiveness while ensuring safety. The framework integrates real-time motion planning with formal safety verification. Evaluated in both simulation and real-world experiments, it achieves a 32% improvement in exploration efficiency, a 57% reduction in planning latency, and a 41% increase in average flight speed over state-of-the-art methods—significantly advancing real-time autonomous exploration capability in highly dynamic 3D environments.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Efficient UAV exploration in large 3D environments
• Reducing computational cost for real-time planning
• Minimizing deceleration from sharp turns during exploration

Innovation

• Dual-layer planning for efficient UAV exploration
• Approximate algorithm for real-time large-scale routing
• Curvature-penalized cost to reduce sharp turns