A Sampling-Based Distributed Exploration Method for UAV Cluster in Unknown Environments

Rapidly completing the exploration and construction of unknown environments is an important task a UAV cluster. However, formulation online autonomous strategy based on real-time detection map still problem that needs to be discussed optimized. In this paper, we propose distributed environment framework for cluster comprehensively considers path terminal state gain, which called Distributed Next-Best-Path Terminal (DNBPT) method. This method calculates gain by calculating new grid brought guidance unexplored area guide UAV’s next decision. We suitable multistep selective sampling improved Discrete Binary Particle Swarm Optimization algorithm optimization. The simulation results show DNBPT can realize rapid under high coverage conditions in multiple scenes.