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.

In this paper, we propose a novel mapping-planning framework for autonomous quadrotor navigation. First, a polyhedron-based mapping algorithm is presented to fully exploit the information of the onboard sensor data. Polyhedra are generated to approximate the segmented clusters of occupied voxels. Then, customized data structures are designed to extract information for motion planning in real time. With complete knowledge of the shape, position, and number of the observed obstacles, we can conveniently generate smooth trajectories with sufficient obstacle clearance along the most desired direction. Before searching for the initial path, a local topological graph is constructed to keep the path expanding in the most favorable topology class. The following path search is segmented based on the graph vertices, which allows fast convergence. The refined trajectory is obtained after smoothing, and large deviations are penalized in the formulated optimization problem to preserve the original clearance. Finally, we analyze and validate the proposed framework through extensive simulations and real-world quadrotor flights.