Digital-Twin-Inspired Autonomous Exploration Framework for Internet of Drones Network

To address the demand of digital twin (DT) model construction, we introduce the autonomous exploration framework for Internet of Drones (IoD) network, to autonomously construct models in the preset areas. However, current multi-UAV autonomous exploration frameworks suffer from low efficiency. To address this, we propose an innovative multi-UAV autonomous exploration framework. Using the LiDAR point cloud, our proposed framework firstly generates mesh frontiers based on the unit sphere point cloud flip, without building an occupancy grid map. Secondly, we design a multi-UAV exploration information interaction method, which can efficiently integrates the information obtained from various UAVs. Based on the information, a sparse topological graph is constructed to store the previous local feasible regions. A heuristic function is used to allocate the exploration targets for UAVs. The simulation results indicate that the proposed method achieves higher efficiency in frontier generation and improves exploration efficiency by 13.3-64.6% compared to the state-of-the-art methods.