论文标题
燃料：使用增量边界结构和分层计划的快速无人机探索
FUEL: Fast UAV Exploration using Incremental Frontier Structure and Hierarchical Planning
论文作者
论文摘要
自主探索是无人驾驶汽车的各种应用的基本问题。但是，由于缺乏有效的全球覆盖范围，保守的运动计划和低决策频率，现有方法被证明是不足的勘探率。在本文中，我们提出了燃料，这是一个层次结构框架，可以支持复杂的未知环境中的快速无人机探索。我们通过Frontier信息结构（FIS）探索计划所需的整个空间中维护关键信息，在探索空间时可以逐步更新。在FIS的支持下，层级规划师计划了三个步骤的探索动作，这些步骤找到了有效的全球覆盖路径，完善了一组本地观点，并以顺序生成最低时间轨迹。我们提出了广泛的基准测试和现实世界测试，其中我们的方法以前所未有的效率完成了探索任务（比最先进的方法相比，要快3-8倍）。我们的方法将成为开源，以使社区受益。
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.
