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.

翻译：本文提出了一种用于自主推车收集任务的同步探索与目标搜索框架。在环境表征方面，我们提出了一种面向任务的环境分区算法，能够为每个子任务提取多样化信息。首先，在去除离群点后，将激光雷达数据分类为潜在目标、墙壁和障碍物。随后，将分割后的点云转换为混合地图，包含以下功能组件：用于避免探索过程中遗漏推车的目标提议；用于语义空间分割的房间布局；以及包含几何信息的多边形障碍物，以实现高效运动规划。针对探索与同步推车收集任务，我们提出了一种基于探索的高效目标搜索方法。首先，通过组合前沿点和目标提议，构建了带优先约束的旅行商问题（TSP-PC）。通过优先搜索目标同时避免机器人过度回溯，选择下一目标点。随后，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框架，并在真实场景的自主推车收集任务中展示了该系统。