FUEL: Fast UAV Exploration Using Incremental Frontier Structure and Hierarchical Planning

• Published in: IEEE robotics and automation letters Vol. 6; no. 2; pp. 779 - 786
• Main Authors: Zhou, Boyu, Zhang, Yichen, Chen, Xinyi, Shen, Shaojie
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerial systems: applications; aerial systems: perception and autonomy; Exploration; Fuels; motion and path planning; Navigation; Planning; Robot sensing systems; Robotics; Space exploration; Structural hierarchy; Trajectory; Trajectory planning; Unknown environments; Unmanned aerial vehicles
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2021.3051563

Autonomous exploration is a fundamental problem for various applications of unmanned aerial vehicles(UAVs). Existing methods, however, were demonstrated to insufficient exploration rate, due to the lack of efficient global coverage, conservative motion plans and low decision frequencies. In this letter, we propose FUEL , a hierarchical framework that can support F ast U AV E xp L oration in complex unknown environments. We maintain crucial information in the entire space required by exploration planning by a frontier information structure (FIS), which can be updated incrementally when the space is explored. Supported by the FIS, a hierarchical planner plans exploration motions in three steps, which find efficient global coverage paths, refine a local set of viewpoints and generate minimum-time trajectories in sequence. We present extensive benchmark and real-world tests, in which our method completes the exploration tasks with unprecedented efficiency (3-8 times faster) compared to state-of-the-art approaches. Our method will be made open source to benefit the community<xref ref-type="fn" rid="fn1"> 1 1 To be released at https://github.com/HKUST-Aerial-Robotics/FUEL . .