Indoor Exploration and Simultaneous Trolley Collection Through Task-Oriented Environment Partitioning

Junjie Gao1,2; Peijia Xie1; Xuheng Gao1; Zhirui Sun1,2; Jiankun Wang1,2; Max Q.-H. Meng1

10.1109/ICRA57147.2024.10610500

2024-05-17

979-8-3503-8458-1

2024 IEEE International Conference on Robotics and Automation (ICRA)

13-17 May 2024

Yokohama, Japan

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.

第一

EI

1.The Department of Electronic and Electrical Engineering, Shenzhen Key Laboratory of Robotics Perception and Intelligence, Southern University of Science and Technology, Shenzhen, China
2.Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China

Junjie Gao,Peijia Xie,Xuheng Gao,et al. Indoor Exploration and Simultaneous Trolley Collection Through Task-Oriented Environment Partitioning[C],2024.