利用微型无人机群对大型未知环境进行探测是未来空间探索发展的新方向。然而，在既无外部电磁信号支持又无内部图像先验信息的情况下，如何有效对纵深距离长、通信条件恶劣、三维地理结构复杂的未知环境实现自动探测就成为现阶段本领域的重要问题。本项目利用微型无人机群作为信息采集平台，交叉融合通信技术、计算机视觉技术、图像处理技术、人工智能技术等各领域知识，深入研究无人机群协作路径规划及动态路由、轻量级三维视觉地图快速构建、无人机群中继节点位置部署以及环境通达性分析与动态障碍实时检测等该领域内的关键科学问题，提出有效解决方法，从而实现无人机群在数量有限、单机通信半径和能量受限条件下，以飞行自组网方式协调有序的开展未知环境探测，并将探测图像可靠稳定的回传并及时生成三维视觉地图。本课题的研究成果将为未来空间探测和目标检测与定位导航领域提供有价值的理论支撑与技术参考方案。

Large unknown environment detection based on micro-UAV group is a new development direction for future space exploration. However, in the case of neither external electromagnetic signal support nor internal image prior information available, how to effectively detect the unknown environment with long depth distance, poor communication condition and complex three-dimensional geographic structure becomes an important issue in this field at the current stage. This project utilizes the micro-UAV group as the information collection platform, cross-integrating technologies of communication, computer vision, image processing, artificial intelligence and other fields, which makes deep study of the UAV group cooperative path planning and dynamic routing, lightweight 3D vision map rapid construction, location deployment of UAV group relay nodes, environmental accessibility analysis and real-time detection of dynamic obstacles, etc. and proposes effective solutions. Therefore, under the condition of limited number, single-unit communication radius and energy limitation, the UAV group can coordinate carry out the unknown environment detection in FANET and backhaul the detected image stably and generate a three-dimensional visual map in time. The research results of this topic will provide valuable theoretical and technical reference solution for future space exploration, target detection and location navigation.