HSGIVS: A Hierarchical Sequence-Guided Method for Efficient UAV Exploration with Interactive Viewpoints Sampling in Unknown Multi-Scale Environment

Unmanned Aerial Vehicles (UAVs) autonomous exploration has shown great potential in emergency rescue, disaster relief and industrial inspection, etc. However, multi-scale environments, such as mines and buildings, pose great challenges to traditional methods sampling viewpoints at a fixed radius. Additionally, the disregard for informative gain and temporal constraints leads to back-and-forth maneuvers (BFMs). To address these problems, an efficient UAV autonomous exploration framework is developed using interactive viewpoints sampling. By introducing longitudinal and lateral sampling, the sampling method can enable the exploration framework to reduce back-and-forth maneuvers in multi-scale scenes. In addition, informative gain based guidance and temporal constraints are proposed. By balancing informative gains against path length and constraining adjacent moments of reverse maneuvers, the consistency of flight trajectories is improved. Both simulation and real world experiments were carried out to verify the effectiveness of proposed method. The results demonstrate that the proposed method outperforms other state-of-the-art approaches by 5.6% to 8.8% across all scenarios, including both general and multi-scale environments.