FUEL: フロンティア情報構造と階層計画による高速UAV探索
FUEL: Fast UAV Exploration using Incremental Frontier Structure and Hierarchical Planning
フロンティア情報構造を逐次更新し、階層的な計画でUAVの未知環境探索を高速化した手法。従来比3〜8倍の効率を実現。
著者: Boyu Zhou, Yichen Zhang, Xinyi Chen, Shaojie Shen
分類: cs.RO
原文アブストラクト
Autonomous exploration is a fundamental problem for various applications of unmanned aerial vehicles. 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 paper, we propose FUEL, a hierarchical framework that can support Fast UAV Exploration 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.
