论文标题
使用混合图表示，在复杂的未知环境中进行自我探索
Self-Exploration in Complex Unknown Environments using Hybrid Map Representation
论文作者
论文摘要
提出了一个由修改的广义Voronoi图（GVD）基于网格的度量图组成的混合图表示，提议促进新的边境驱动的探索策略。勘探前沿是开放空间和未开发空间之间边界的区域。移动机器人能够通过添加新空间并移至未访问的边界来构建其地图，直到探索整个环境为止。由于缺乏确定和分配最佳勘探命令的系统性方法，现有的勘探方法在复杂环境中的勘探效率较低。提出了从GVD地图（Global）和本地滑动窗口中检测到的前沿的抽象信息，提出了一种全局本地探索策略，以层次结构方式处理勘探任务。新的探索算法能够创建一个修改的树结构来表示环境，同时在自我探索过程中整合全球边界信息。在模拟环境中验证了所提出的方法，然后在现实世界的办公环境中进行了测试。
A hybrid map representation, which consists of a modified generalized Voronoi Diagram (GVD)-based topological map and a grid-based metric map, is proposed to facilitate a new frontier-driven exploration strategy. Exploration frontiers are the regions on the boundary between open space and unexplored space. A mobile robot is able to construct its map by adding new space and moving to unvisited frontiers until the entire environment has been explored. The existing exploration methods suffer from low exploration efficiency in complex environments due to the lack of a systematical way to determine and assign optimal exploration command. Leveraging on the abstracted information from the GVD map (global) and the detected frontier in the local sliding window, a global-local exploration strategy is proposed to handle the exploration task in a hierarchical manner. The new exploration algorithm is able to create a modified tree structure to represent the environment while consolidating global frontier information during the self-exploration. The proposed method is verified in simulated environments, and then tested in real-world office environments as well.
