Path planning for LiDAR equipped UGV considering coverage and efficiency in building inspection

Construction quality inspection plays a crucial role in ensuring safety and quality at all stages of a project. However, traditional quality inspection has certain limitations, such as time consuming, expensive equipment, and cumbersome processes. Therefore, improving inspection technologies and processes by utilizing Unmanned Ground Vehicle (UGV) equipped with relatively low-cost LiDAR to enhance data collection coverage and efficiency during inspections has become a pressing issue in current construction quality management. The main objective of this paper is to explore methods for enhancing the data acquisition capabilities of UGV equipped with low-cost LiDAR in construction environments and to experimentally verify the impact of path planning methods on data accuracy. The research focuses on investigating a new algorithm that simulates LiDAR scanning by UGV in point cloud space through 3D modeling and iteratively selects paths using Genetic Algorithm (GA) to preserve the path with the highest point cloud data collection efficiency. The experimental results extract the coordinates of the selected paths based on different scenarios, providing data support for the application of UGV on construction sites and promoting the adoption of intelligent building inspection methods.