关键词: 关键词: 多无人机系统, 自主探索, 大范围环境, 轨迹优化, 探索策略

Abstract: Abstract: To address the problems of low exploration efficiency, key information exchange under limited communication bandwidth in the current Multiple Unmanned Aerial Vehicle (Multi-UAV) system when exploring large-scale complex environments, a fast and fully autonomous exploration method for Multi-UAV system in large-scale complex environments was proposed, which included a fast and hierarchical exploration strategy and a lightweight large-scale environment modeling method. First, closed viewpoints were generated in the front-end trajectory planning to drive the UAV to explore unknown environments. Then, the smooth, continuous, and time-optimal trajectory optimization was transformed into a convex optimization problem in the back-end. Besides that, for large-scale environments, a random mapping method was used for lightweight mapping and map data exchange. In simulation, the proposed method was compared with three existing approaches: FUEL(Fast Unmanned aerial vehicle ExpLoration), FBE(Frontier-Based Exploration), NBVP(Next Best View Planner). The results showed that the performance of the proposed method in terms of exploration time is improved by 14.4%, 43.9% and 47.7%, respectively. The lightweight mapping method used 28.3% and 22.4% less data than the Bayesian method and the polyhedron method. Combined with the results of the exploration experiment in terms of speed and data volume, the proposed method can perform fast and fully autonomous exploration in large-scale complex environments.

Key words: Keywords: Multi-UAV System, Autonomous Exploration, Large-Scale Environment, Trajectory Optimization, Exploration Strategy