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Long Duration Inspection of GNSS-Denied Environments with a Tethered UAV-UGV Marsupial System

29 May 2025
Simón Martínez-Rozas
David Alejo
José Javier Carpio
F. Caballero
L. Merino
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Abstract

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 novel 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. We detail the hardware architecture based on off-the-shelf components to ensure replicability and describe our full-stack software framework, 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 field experiments: (1) a manual flight endurance test to estimate the operational duration, (2) an autonomous navigation test, and (3) an inspection mission to demonstrate autonomous inspection capabilities. Experimental results confirm the robustness and autonomy of the system, its capacity to operate in GNSS-denied environments, and its potential for long-endurance, autonomous inspection and monitoring tasks.

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@article{martínez-rozas2025_2505.23457,
  title={ Long Duration Inspection of GNSS-Denied Environments with a Tethered UAV-UGV Marsupial System },
  author={ Simón Martínez-Rozas and David Alejo and José Javier Carpio and Fernando Caballero and Luis Merino },
  journal={arXiv preprint arXiv:2505.23457},
  year={ 2025 }
}
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