采用系留式无人机-无人车有袋系统的拒止GNSS环境长时巡检
Long Duration Inspection of GNSS-Denied Environments with a Tethered UAV-UGV Marsupial System
AI总结：
针对GNSS拒止环境下无人机长时巡检续航不足的问题，本文提出系留式UAV-UGV有袋机器人系统，通过软硬件设计实现长时自主巡检，经野外实验验证性能并公开数据集。
摘要：
无人机（UAV）凭借高机动性与可抵达难进入区域的能力，已成为巡检与应急响应作业中的核心工具，但其有限的续航能力严重制约了长时任务场景下的应用。本文提出一种由无人机与无人地面车辆（UGV）组成的系留有袋机器人系统，专门面向全球导航卫星系统（GNSS）拒止环境下的自主长时巡检任务设计。该系统通过连接无人车搭载的大容量电池组的系留线缆为无人机供电，大幅延长无人机的作业时长。本文详细介绍了基于商用现货组件搭建、可确保可复现性的硬件架构，同时阐述了系统采用的全栈软件框架：该框架由开源组件构成，基于机器人操作系统（ROS）搭建。所提出的软件架构可通过直接激光雷达定位（DLL）方法实现精准定位，同时为无人车-系留线缆-无人机集成系统提供安全路径规划与协同轨迹跟踪能力。我们通过三组野外实验对系统进行验证：（i）三次手动飞行续航测试以评估作业时长；（ii）三次定位与轨迹跟踪系统验证实验；（iii）三次巡检任务执行以演示自主巡检能力。实验结果证实了该系统在GNSS拒止环境下的鲁棒性与自主性。最后，所有实验数据均已公开，以支持研究可复现性，并作为基准测试的通用开放数据集。
英文摘要：
Unmanned Aerial Vehicles (UAVs) have become essential tools in inspection and emergency response operations due to their high maneuverability and ability to access hard-to-reach areas. However, their limited battery life significantly restricts their use in long-duration missions. This paper presents a tethered marsupial robotic system composed of a UAV and an Unmanned Ground Vehicle (UGV), specifically designed for autonomous, long-duration inspection tasks in Global Navigation Satellite System (GNSS)-denied environments. The system extends the UAV's operational time by supplying power through a tether connected to high-capacity battery packs carried by the UGV. Our work details the hardware architecture based on off-the-shelf components to ensure replicability and describes our full-stack software framework used by the system, which is composed of open-source components and built upon the Robot Operating System (ROS). The proposed software architecture enables precise localization using a Direct LiDAR Localization (DLL) method and ensures safe path planning and coordinated trajectory tracking for the integrated UGV-tether-UAV system. We validate the system through three sets of field experiments involving (i) three manual flight endurance tests to estimate the operational duration, (ii) three experiments for validating the localization and the trajectory tracking systems, and (iii) three executions of an inspection mission to demonstrate autonomous inspection capabilities. The results of the experiments confirm the robustness and autonomy of the system in GNSS-denied environments. Finally, all experimental data have been made publicly available to support reproducibility and to serve as a common open dataset for benchmarking.
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