通过面向任务的环境划分实现室内探索与同步手推车收集
Indoor Exploration and Simultaneous Trolley Collection Through Task-Oriented Environment Partitioning
AI总结：
本文提出一种面向任务的环境划分与同步探索搜索框架，通过构建混合地图和带优先约束的旅行商问题，实现自主手推车收集中的高效探索与物体搜索。
摘要：
在本文中，我们提出了一种用于自主手推车收集应用的同步探索与物体搜索框架。在环境表示方面，提出了一种面向任务的环境划分算法，以提取每个子任务的多样化信息。首先，在去除离群点后，将LiDAR数据分类为潜在物体、墙壁和障碍物。然后，将分割后的点云转换为具有以下功能组件的混合地图：物体候选区域，以避免在探索过程中遗漏手推车；房间布局，用于语义空间分割；以及包含几何信息的多边形障碍物，用于高效的运动规划。对于探索与同步手推车收集，我们提出了一种高效的基于探索的物体搜索方法。首先，通过将前沿区域和物体候选区域分组，构建了一个带优先约束的旅行商问题（TSP-PC）。下一个目标的选择优先考虑物体搜索，同时避免机器人过度回溯。然后，通过拓扑图搜索生成具有足够障碍物间隙的可行轨迹。我们通过仿真验证了所提出的框架，并在真实世界的自主手推车收集任务中演示了该系统。
英文摘要：
In this paper, we present a simultaneous exploration and object search framework for the application of autonomous trolley collection. For environment representation, a task-oriented environment partitioning algorithm is presented to extract diverse information for each sub-task. First, LiDAR data is classified as potential objects, walls, and obstacles after outlier removal. Segmented point clouds are then transformed into a hybrid map with the following functional components: object proposals to avoid missing trolleys during exploration; room layouts for semantic space segmentation; and polygonal obstacles containing geometry information for efficient motion planning. For exploration and simultaneous trolley collection, we propose an efficient exploration-based object search method. First, a traveling salesman problem with precedence constraints (TSP-PC) is formulated by grouping frontiers and object proposals. The next target is selected by prioritizing object search while avoiding excessive robot backtracking. Then, feasible trajectories with adequate obstacle clearance are generated by topological graph search. We validate the proposed framework through simulations and demonstrate the system with real-world autonomous trolley collection tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
