Development of Tributary Mapping System using Multiple Unmanned Aerial Vehicles with LiDAR

This paper proposes a hybrid system comprising multiple unmanned aerial vehicles (UAVs) to monitor nature. The proposed tributary mapping system consists of perception and control systems. A perception system is established for tributary mapping using three-dimensional light detection and ranging (LiDAR) semantic segmentation for surface water recognition. The system defines a water point cloud and segments the surface water area using singular value decomposition, applying the water-absorbing property of LiDAR. A path is generated through random sample consensus by calculating the center point from the segmented area. Each UAV is modeled using a continuous-time dynamic-based low-level model and a discrete event-time dynamic-based high-level model to control the multiple UAVs system. The desired plant behavior is designed considering the control objectives of the plant, and a supervisory controller is developed accordingly. A physics-based robot simulator is employed to verify the proposed tributary mapping system in perception and path generation and the performance of the hybrid system-based supervisory controller.