Unmanned aerial vehicles (UAVs) have been widely used in industry and daily life, where safety is the primary consideration, resulting in their use in open outdoor environments, which are wider than complex indoor environments. However, the demand is growing for deploying UAVs indoors for specific tasks such as inspection, supervision, transportation, and management. To broaden indoor applications while ensuring safety, the quadrotor is notable for its motion flexibility, particularly in the vertical direction. In this study, we developed an improved probabilistic roadmap (PRM) planning method for safe indoor flights based on the assumption of a quadrotor model UAV. First, to represent and model a 3D environment, we generated a reduced-dimensional map using a point cloud projection method. Second, to deploy UAV indoor missions and ensure safety, we improved the PRM planning method and obtained a collision-free flight path for the UAV. Lastly, to optimize the overall mission, we performed postprocessing optimization on the path, avoiding redundant flights. We conducted experiments to validate the effectiveness and efficiency of the proposed method on both desktop and onboard PC, in terms of path-finding success rate, planning time, and path length. The results showed that our method ensures safe indoor UAV flights while significantly improving computational efficiency.

Today, natural disasters have a huge impact all over the world, while GNSS plays an important role in disaster relief and rescue. However, when the ground surface is severely damaged and covered, satellite positioning means are denied. In addition, disaster site conditions are often very complex and may require unmanned robots such as UAVs for pre-surveying. To address the raised problem, we reconstructed the 3D scene by laser SLAM; improved PRM path planning method for better computational efficiency while solving feasible path results; and realized UAV autonomous flight along the planned path in GNSS-denied environment. The experiments prove that the reconstructed scene map provides a feasible means for UAV autonomous navigation in GNSS-denied environment, and the proposed path planning method has a significant improvement in computational efficiency.