A comparison of sensors for underground void mapping by unmanned aerial vehicles

Abstract

Unmanned aerial vehicles (UAVs) are being employed in a rapidly increasing number of applications in mining, including the emerging area of mapping underground void spaces such as stopes, which are otherwise inaccessible to humans, automated ground vehicles and survey technologies. Void mapping can provide both visual rock surface and 3D structural information about stopes, supporting more effective planning of ongoing blast designs. Underground stope mapping by UAVs, however, involves overcoming a number of engineering challenges to allow flights beyond operator line-of-sight where there is no global positioning system (GPS), natural or artificial light, or existing communications infrastructure. This paper describes the construction of a UAV sensor suite that uses sound navigation and ranging (SONAR) data to create a rough 3D model of the underground UAV operational environment in real time to provide operators with high situational awareness for beyond line-of-sight operations. The system also provides a backup when dust obscures visual sensors to provide situation awareness and a coarser, but still informative, 3D model of the underground space. Typically, light detection and ranging (LIDAR) systems have superseded SONAR sensors for similar applications. LIDAR is much more accurate than SONAR, but has several disadvantages. SONAR sensor data is sparse, and therefore much easier to process in real time on-board the UAV than LIDAR. The SONAR sensor hardware is also lighter than current LIDAR systems, which is of importance regarding the constrained payload capacity of UAVs. However, the most important factor that makes SONAR stand out in this application is its ability to operate in dusty or smoke-filled environments. The UAV system was tested both above and below-ground using a predefined path with check point locations for the UAV to follow. Due to the lack of GPS, survey points in combination with photogrammetry allowed the UAV's location to be estimated. This allowed the system to be tested to determine how accurate the SONAR data is in comparison with 3D modelling via photogrammetry of images from a separate digital single-lens reflex camera. Comparing the shape of void surfaces determined by photogrammetry with that determined by SONAR provides quantifiable accuracy when the photogrammetry models are used as ground truth data. Above-ground and underground pilot studies have determined that SONAR sensors provide acceptable accuracy compared with modelling via photogrammetry, sufficient to provide effective situational awareness for human operation of the UAV beyond line-of-sight.

A Comprehensive Review of Applications of Drone Technology in the Mining Industry

“…Drones in underground mines have numerous potential applications in health and safety. These applications include surface roughness mapping, rock mass stability analysis, ventilation modeling, hazardous gas detection, and leakage monitoring [32,[63][64][65][66][67][68]].…”

Towards a novel auto-rotating lidar platform for cavity surveying

“…The confined spaces, dampness, reduced visibility, air movement and lack of control signal propagation are obstacles that hinder the operation of UAVs underground [22]. This has prompted researchers to develop unique algorithms and hardware to successfully navigate UAVs autonomously underground [24,22,2]. Turner [24] has conducted preliminary research towards autonomous rock face mapping with the Astec Pelican UAV and Sennersten et al [22,2] has conducted initial trials of beyond line of sight control with an underground UAV.…”

“…This has prompted researchers to develop unique algorithms and hardware to successfully navigate UAVs autonomously underground [24,22,2]. Turner [24] has conducted preliminary research towards autonomous rock face mapping with the Astec Pelican UAV and Sennersten et al [22,2] has conducted initial trials of beyond line of sight control with an underground UAV. At the time of writing, there is not a commercially available fully autonomous solution to cavity surveying using UAVs.…”

Path Planner for Autonomous Exploration of Underground Mines by Aerial Vehicles

“…Although cameras have been widely used in robotic exploration, since they provide rich data about the surroundings which is invaluable for robust navigation, they present relevant challenges in non-illuminated or visually homogeneous environments. Thus, previous works with aerial robots commonly use some kind of range finding technology as the primary sensor, whether it be LIDAR 2D [6], LIDAR 3D [7] or sound navigation and ranging (SONAR) [8]. They are always combined with an inertial system unit (INS) and, on some occasions, assisted by visual stereo cameras [4] or optical flow sensors (required illumination provided by on-board lights) [9].…”

“…The process to define robot movements along the mine (path planning) varies from global to local strategies. When global planning is used, it is augmented with some kind of local adjusting, e.g., dynamic insertion of new obstacles [2], a wavefront-based global planner with local vector field histogram analysis in [12] or a predefined path with local checkpoint marks [8]. The alternative is making use of the pure local planning, which usually requires less processing resources.…”