Successful Autonomous Flight Test in Open Pit Mine

In the quest to optimise mine monitoring and inspection through advanced robotics, the European Union-funded M4Mining project has launched groundbreaking field tests. As part of this initiative a team from the Robotics and AI group successfully carried out drone-based experiment at a bauxite open-pit mine near Itea in Greece.

Vignesh Kottayam Viswanathan, Doctoral Student, Vidya Sumathy, Postdoctoral Researcher, and Ilias Tevetzidis, Senior Research Engineer, Christoforos Kanellakis, Assistant Professor and George Nikolakopoulos, Professor, are all working with the M4Mining (Multi-scale, Multi-sensor Mapping and dynamic Monitoring for sustainable extraction and safe closure in Mining environments) project. The project aims to develop a system to keep an eye on mining sites, both working and abandoned, utilising drones and satellites.

"By capturing detailed images and measurements with the drones and satellites it will optimise how we identify materials. This system will work in real-time which makes it easier to monitor mine areas and manage waste while also helping mining become more efficient and eco-friendlier", Vidya Sumathy says.

Objectives of the field visit

The field tests conducted by the Robotics and AI team had two main objectives. The primary objective was to evaluate an adaptive, autonomous flight path planning algorithm developed by Vignesh. The flight path was specifically designed to tackle the irregular surfaces of mine faces. As the drone inspected the mine face, it autonomously updated its flight path based on real-time sensor data, maintaining precise and consistent observation distances across the complex, evolving surface.

The secondary objective was to design an automated flight path specifically for the Itea mine face for the drone developed in the project. The flight path was developed using Vignesh’s framework, an optimised static flight plan was generated in a simulated environment. This static route, created to adhere to specific viewing constraints, was subsequently tracked by the project partner’s drone to capture sensor data.

"For the field testing we used drones, specifically the DJI Matrice 300 drone which is commercially available. But drones in general contributes to many aspects, for instance safety. The drone is expandable and can do the dirty and dangerous work instead of humans. Drones are also more consistent in executing tasks, which makes the results more consistent too", Ilias Tevetzidis says.

The Importance of Inspecting Irregular Surfaces

Traditional UAV flight plans assume a flat, static mine face, which leads to inconsistent inspection distances and may leave uninspected gaps or holes in 3D reconstructions. By contrast, Vignesh’s adaptive flight planner was designed to continuously track surface changes by analysing the 3D LiDAR point-cloud measurements in real time.

"This innovation allows the UAV to maintain a constant distance from the mine face, automatically adjusting its flight path based on real-time conditions, effectively closing inspection gaps and creating a more comprehensive 3D model", Vignesh Kottayam Viswanathan says.

The recent autonomous flight experiments at the Itea mine represent a breakthrough in the M4Mining project’s mission to advance remote sensing and robotics in mining. By achieving successful results with an adaptive and automated flight planner, M4Mining has demonstrated that UAV technology can be adapted to meet the unique challenges of mining, setting a new standard for safety, accuracy, and efficiency in mine inspection. The insights gained from these first of its kind field experiments are crucial to supporting sustainable and safe mining practices, emphasizing the EU’s commitment to innovation in environmental monitoring and resource management.