Oxpecker: A Tethered UAV for Inspection of Stone-Mine Pillars

Abstract

This paper presents a state-of-the-art tethered unmanned aerial vehicle (TUAV) for structural integrity assessment of underground stone mine pillars. The TUAV, powered by its tether, works in tandem with an unmanned ground vehicle (UGV) that hosts the TUAV batteries, a self-leveled landing platform, and the tether management system. The UGV and the TUAV were named Rhino and Oxpecker, respectively, given that the TUAV stays landed on the UGV while the ensemble moves inside a mine. The mission of Oxpecker is to create, using a LiDAR sensor, 3D maps of the mine pillars to support time-lapse hazard mapping and time-dependent pillar degradation analysis. Given the height of the pillars ( 7 – 12m), this task cannot be executed by Rhino alone. This paper describes the drone’s hardware and software. The hardware includes the tether management system, designed to control the tension of the tether, and the tether perception system, which provides information that can be used for localization and landing in global navigation satellite systems (GNSS)-denied environments. The vehicle’s software is based on a state machine that controls the several phases of a mission (i.e., takeoff, inspection, and landing) by coordinating drone motion with the tethering system. The paper also describes and evaluates our approach for tether-based landing and autonomous 3D mapping of pillars. We show experiments that illustrate and validate our system in laboratories and underground mines.

Long-Range 3D Reconstruction Based on Flexible Configuration Stereo Vision Using Multiple Aerial Robots

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“…Specifically in recent years, multi-rotor platforms, due to their mobility in deployment and operation, allow for their use in smallto medium-sized areas of operation. Various approaches to 3D reconstruction and mapping utilising one or a fusion of sensors using multi-rotor platforms exist, such as photogrammetry and structure from motion (SfM) for medium-to high-altitude mapping [2,3], light detection and ranging (LiDAR)-based 3D mapping [4][5][6], and visual simultaneous localisation and mapping (SLAM)-based global navigation satellite system (GNSS)-denied area mapping [7,8].…”

Quantitative and Qualitative Analysis of Agricultural Fields Based on Aerial Multispectral Images Using Neural Networks

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Active manipulation of a tethered drone using explainable AI

“…Each drone uses a distributed linear controller, and a high-level model predictive controller is employed to control the formation. On the other hand, Martinez Rocamora Jr et al [29] develop "Oxpecker", a tethered drone for inspection of stone-mine pillars. A multi-agent Q-learning approach is examined in another research [17] for using an optimal tethered drone in A2G communication network.…”