Exploration systems are critical for enhancing the autonomy of robots. Due to the unpredictability of the future planning space, existing methods either adopt an inefficient greedy strategy or require a lot of resources to obtain a global solution. In this work, we address the challenge of obtaining global exploration routes with minimal computing resources. A hierarchical planning framework dynamically divides the planning space into subregions and arranges their orders to provide global guidance for exploration. Indicators that are compatible with the subregion order are used to choose specific exploration targets, thereby considering estimates of spatial structure and extending the planning space to unknown regions. Extensive simulations and field tests demonstrate the efficacy of our method in comparison to existing 2D LiDAR-based approaches. Our code has been made public for further investigation.

翻译：探测系统对于提升机器人自主性至关重要。由于未来规划空间的不可预测性，现有方法要么采用低效的贪婪策略，要么需要大量资源获取全局解。本研究致力于解决以最小计算资源获取全局探测路径的挑战。我们提出一种层级规划框架，该框架将规划空间动态划分为子区域，并通过排列子区域顺序为探测提供全局引导。采用与子区域顺序兼容的指标选择具体探测目标，从而兼顾空间结构估计并将规划空间扩展至未知区域。大量仿真与实地实验表明，与现有基于二维激光雷达的方法相比，本方法具有显著有效性。相关代码已开源以供进一步研究。