从草图先验到轨迹：一种面向任务的室内无人机群协同导航框架
From Sketch Prior to Trajectories: A Mission-Oriented Coordinated Navigation Framework for Indoor UAV Swarm
- School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对室内无人机群任务，提出面向任务的协同导航框架，利用草图先验，经拓扑对齐、融合观测构建可通行性表示，开发分层二维 - 三维框架，经仿真和实验验证其有效性、协同导航能力及对分层结构的可扩展性。
AI中文摘要：
用于室内检查、安全巡逻和物流配送等应用的无人机群通常是面向任务而非探索的。在这些任务中，无人机需按规定顺序访问与任务相关区域，此类区域级任务信息常可从预部署草图地图先验（如平面图、CAD布局或疏散图）获取。尽管任务在三维空间执行，但无人机通常在特定高度层或每层近乎固定的高度范围内飞行，使任务级区域转换主要由平面连通性控制。基于这些观察，本文提出一种面向任务的协同导航框架，利用草图地图先验进行多无人机室内操作。机载观测用于进行拓扑对齐，对齐后的先验与在线观测融合以构建面向任务的可通行性表示。进一步开发了分层二维 - 三维协同导航框架，其中二维引导路径规划生成面向任务的引导路径，引导驱动的三维轨迹优化产生动态可行且无碰撞的轨迹。仿真和实际实验验证了该框架在结构化多房间室内环境中的有效性，并进一步展示了其在通信可用和通信丢失条件下的协同导航能力。多层仿真结果显示了系统对分层室内结构的可扩展性。
英文摘要：
UAV swarm for applications, such as indoor inspection, security patrol, and logistics delivery, are often mission-oriented rather than exploration-oriented. In these tasks, UAVs are required to visit task-relevant regions in a prescribed sequence, and such region-level mission information can often be obtained from pre-deployment sketch-map priors, such as floor plans, CAD layouts, or evacuation diagrams. Although these tasks are executed in three-dimensional space, UAVs usually fly within a specific altitude layer or a nearly fixed altitude range on each floor, making mission-level region transitions mainly governed by planar connectivity. Based on these observations, this paper proposes a mission-oriented coordinated navigation framework that exploits sketch-map priors for multi-UAV indoor operations. Onboard observations are used to perform topological alignment, and the aligned prior is fused with online observations to construct a mission-oriented traversability representation. A layered 2D--3D coordinated navigation framework is further developed, where 2D guided path planning generates mission-oriented guide paths and guide-driven 3D trajectory optimization produces dynamically feasible and collision-free trajectories. Simulation and real-world experiments validate the effectiveness of the proposed framework in structured multi-room indoor environments and further demonstrate its coordinated navigation capability under both communication-available and communication-loss conditions. Multi-floor simulation results show the scalability of the system to layered indoor structures.
