发表机构
Jinan University; Beijing Institute of Technology; University of Chinese Academy of Sciences（暨南大学; 北京理工大学; 中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种未知环境下的在线分层覆盖路径规划算法，通过增量分解树组织子区域并动态更新全局覆盖路线，仿真验证其相比三种基线算法在路径长度和重叠率上提升了覆盖效率。
AI 中文摘要
本文提出了一种针对未知环境的在线覆盖路径规划算法。在导航过程中，随着新障碍信息的获取和覆盖的推进，初始未知的搜索区域被逐步分解为多个不连通的子区域。这些子区域通过增量构建的分解树进行组织，该树保留了它们之间的层级父子关系。基于这棵树，通过根据新生成的子区域的探索状态和与机器人的距离对其进行优先级排序，全局覆盖路线得以在线维护和更新。随后，局部规划器在每个选定的子区域内生成覆盖运动，使机器人能够随着环境的逐步显现而调整其轨迹。该算法的性能通过在复杂场景中的高保真仿真进行了评估。结果表明，与三种基线算法相比，该算法在路径长度和重叠率方面提高了覆盖效率。
英文摘要
This paper presents an online coverage path planning algorithm for unknown environments. During navigation, the initially unknown search area is progressively decomposed into disconnected subareas as new obstacle information is acquired and coverage proceeds. These subareas are organized in an incrementally constructed decomposition tree that preserves their hierarchical parent-child relationships. Based on this tree, a global coverage tour is maintained and updated online by prioritizing newly generated child subareas according to their exploration states and distances from the robot. A local planner then generates coverage motions within each selected subarea, allowing the robot to adapt its trajectory as the environment is gradually revealed. Its performance is evaluated via high-fidelity simulations in complex scenarios. The results show improved coverage efficiency in terms of path length and overlap ratio in comparison to three baseline algorithms.
