FUEL: Fast UAV Exploration using Incremental Frontier Structure and Hierarchical Planning
This addresses the need for faster and more efficient UAV exploration in applications like mapping and search, though it appears incremental as it builds on existing frontier-based methods.
The paper tackles the problem of slow autonomous UAV exploration in unknown environments by proposing FUEL, a hierarchical framework that uses an incremental frontier structure and three-step planning, achieving 3-8 times faster exploration compared to state-of-the-art methods.
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.