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ERE4.4

EDI
Automation and robotics for raw material exploration and production in Europe

Research and innovation in exploration and mining of raw materials is increasingly focused on the prospect of developing completely new methods and technologies to find and exploit new mineral deposits within Europe. Amongst these technologies, robotisation and miniaturisation of exploration/production platforms (robotic autonomous explorers & miners) allow to reconsider “non-economical” deposits (abandoned, small, ultra-depth), extract them in a socially and environmentally responsible way, and produce useful metallurgical products which can be used further-on for manufacturing.
Underground operation of an autonomous or semi-autonomous underground platform is an extremely challenging problem where solution have to come from the close collaboration of robotic engineers, mining engineers, mineralogists, geochemists, geophysicians and structuralists to solve challenges as diverse as locomotion in water or slurries, localization and mapping in relationship with an orebody, automated extraction planning, optimization of extraction tools, and real-time selective mineralogy.
Contributions from geologists, geophysicists, mining engineers, robotic engineers, software developers are welcome.

Co-organized by NP8
Convener: Giorgia StasiECSECS | Co-conveners: Claudio Rossi, Eva Hartai, Michael BernerECSECS
Presentations
| Mon, 23 May, 11:05–11:50 (CEST)
 
Room 0.96/97

Mon, 23 May, 10:20–11:50

Chairpersons: Giorgia Stasi, Michael Berner

11:05–11:10
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EGU22-10299
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ECS
Zorana Milosevic et al.

It is estimated that there are more than 8000 abandoned, flooded mines in Europe, many of which lack any information on their status and layout. Accurate and detailed 3D modeling plays a key role in fully understanding these complex environments and determining their remaining hidden potential. However, acquiring the needed data is a challenging task since these environments are extremely hazardous for traditional methods such as human diving. Additionally, human divers can reach only a limited depth range, much smaller than that of a standard mine. Therefore, underwater vehicles appear as a natural alternative for overcoming the disadvantages of direct human exploration. The UX1-Neo is a semi-autonomous underwater robotic system built precisely for this use. This small spherical robot with a 0.7m diameter has a 500m depth rating and various sensors for surveying the environment, such as multibeam and scanning sonars, structured light projectors, and multispectral cameras. 

 

The unfavorable properties of the water medium, such as light scattering and attenuation, pose additional difficulties for data acquisition in these complex environments. Furthermore, mine tunnels are a GPS-denied environment, which makes the modeling system rely entirely on the robot's inertial navigation system, which is prone to error due to the dead-reckoning drift. Conventional methods for correcting this drift, such as SLAM, face additional challenges in these repetitive environments (shafts and tunnels) due to their highly symmetric structures and lack of distinctive features. Additionally, during the exploration of a salt mine, Solotvyno (Ukraine), we faced a new challenge, a refraction of the sonar data due to the salty water, which required further processing in order to create an accurate 3D map of the mine. 

 

Rapid developments in the field of underwater photogrammetry are producing impressive results; however, they still have difficulties with the environments with low light, which causes blurring of details, low image contrast, and in general, lack of features needed for image matching. Also, underwater images are prone to contain an excessive amount of blue light, making the features even less visible. Moreover, photogrammetry technology struggles with repetitive environments due to the same reasons as SLAM.

 

In this work, we demonstrate the challenges faced during our exploration of the Solotvyno salt mine with the UX1-Neo robot and how we overcame them in order to produce a detailed 3D model. In particular, we illustrate that sensor and data redundancy is vital during operations and post-processing. Each UX1-Neo sensor contributes to a complete, coherent picture of the environment. However, using many sensors produces an enormous amount of data that require further filtering: hundreds of millions of points are reduced to a few million using both automated and manual methods. Images also require processing due to the aforementioned reasons: using CLAHE contrast enhancement together with white balancing algorithms, we produce suitable images for photogrammetry. Additionally, data gathered from multiple missions need to be combined for a complete model: we show the importance of robot orientation initialization and external surveying of the robot's launch location to correctly align scans of different missions.

How to cite: Milosevic, Z., Papp, R. Z., and van Moerkerk, H.: Overcoming the challenges of 3D modeling in harsh, confined, underwater environments: A case study, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10299, https://doi.org/10.5194/egusphere-egu22-10299, 2022.

11:10–11:15
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EGU22-7427
Márcio Pinto et al.

UNEXUP is a project co-funded by EIT RawMaterials that started in January 2020 and will be concluded in December 2022. The main objective is to develop, test, and commercialize a novel robot-based technology to survey flooded mines and other underwater structures. The robots are equipped with geoscientific and navigation instruments that allow the collection of valuable data from sites that cannot be assessed without human risks or high investments for dewatering, for example.

This technology was initially developed during the H2020 UNEXMIN project – UNEXUP predecessor, during which three (UX-1) robots were built and tested in five different underwater sites in Europe with increasingly challenging conditions. From the lessons learned on these pilot tests, the engineers collected crucial points for improvement – in close connection with the feedback and requirements from potential users of technology.

In UNEXUP the objective is to build two new robots, with improved software and hardware compared to the previous generation, and to launch them to the market as a commercial service. The first robot, UX-1Neo, was developed in 2020; while UX-2 will be ready in 2022.

UX-1Neo is the upscaled version of the UX-1, equipped with improved navigational and geoscientific instruments and sensors. The upscaling robot has performed four field missions in 2021 – ranging from flooded mines, a water well, and an underwater cave.

The field missions proved the added value that the technology can provide to the mining community and other sectors involving underwater structures. UX-1Neo is a modular vehicle, ca. 90 kg, with swappable batteries, autonomy of approximately 9 hours, and depth capacity of 500 m. An IMU and DVL support the navigation of the robot, to measure the position and depth during the missions. The multibeam (1) and scanning sonars (2) allow the robot to map close, mid, and long-range cavities, and to detect and avoid obstacles in the environment. In addition, the robot is equipped with six SLSs (Structured Light Systems) for more detailed mapping when visibility and turbidity allow. Six cameras – natural light – allow the visualization of the environment and identification of rock types and geological structures. The motion control is supported by eight thrusters, and a mechanical pendulum, for pitch position lock.

The geoscientific instrumentation in UX-1Neo includes a hyperspectral unit, water sampler unit, water chemistry unit (pH, oxygen concentration, EC, temperature, pressure), sub-bottom profiler and a fluxgate magnetometer. This payload allows geoscientists to collect and interpret spatial and geoscientific data from underwater sites.

UX-2 is being developed with increased modularity and depth range compared to UX-1Neo, and some instruments and sensors in UX-1Neo were designed to be compatible with UX-2. It will have higher Technology Readiness Level; and a rock sampling unit supported by a robotic arm. Therefore, the UX-2 will be able to perform in even more challenging environments – broadening the applications of the commercial service – and extending its reliability to perform.

How to cite: Pinto, M., Zajzon, N., Lopes, L., Bodo, B., Henley, S., Almeida, J., Aaltonen, J., and Žibret, G.: UNEXUP, towards the exploration of underwater environments with a robotic solution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7427, https://doi.org/10.5194/egusphere-egu22-7427, 2022.

11:15–11:20
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EGU22-11184
Hilco van Moerkerk and Paulina Dobrowolska

Within the EU funded Horizon 2020 project ROBOMINERS (www.robominers.eu) we were challenged to consider how autonomous robotic mining could be integrated with geological modelling. How would an autonomous robotic miner know where and whether an orebody is worth extracting? The orebody and all related geological aspects would need to be modelled in a comprehensible, self-containing format the robot can use directly.  To this end we envisioned a robot that would know where the orebody is, its important characteristics, and would have the ability to interpret the orebody in real-time and update its geological knowledge of the orebody as it excavates.

The modelled orebody could only be approximate at the robot scale (estimated at 1m maximum diameter!) as detailed information would be lacking. This led to re-evaluating existing geological modelling practices to see how they would fit within the robotic mining concept.

In our work we developed a novel approach to traditional geological modelling by combining three essential elements:

  • Replacement of blocks in block modelling with tetrahedra
  • Functional modelling framework to create model descriptors
  • Machine Learning

A tetrahedron is the most basic 3D element, similar to a triangle being the most basic 2D element to represent objects. Tetrahedra can be made to accurately reflect a boundary and are therefore always either inside or outside of that boundary. They are commonly used in Finite Element Methods (FEM) and have found their way into geophysics, geomechanics and flow modelling, but until now, not into geological modelling. Another major advantage is that a tetrahedral grid can be constructed at multi-resolution scales. However, it also means geological features need to be described in a way that allows them to be represented at those scales (e.g. mine scale versus robot scale).

One method to deal with these scale issues is to use a functional representation: representing geological features with (mathematical) functions. With functions, a value from that function at ANY point in 3D space can be retrieved to see if  that point is either inside or outside of a unit. Functions have been used under the Implicit Modelling (IM) banner. However, the functions can be also seen as classifiers between regions. Machine Learning’s (ML) core functionality is to provide is a mechanism for classifying data and estimate values or labels to unknown points. In our work, were therefore integrated high performance ML algorithms into IM.

With these three key elements we developed a system that can represent a complete 3D geological model in a consistent and ordered way by describing it rather than actually creating it. The model description can then create an optimized FEM model at any resolution when needed, even though the descriptor does not change. The ultimate aim is that the descriptors and functions will be used directly by the robot to optimize its path planning, without needing large data transfers.

How to cite: van Moerkerk, H. and Dobrowolska, P.: Robotic mining: a new approach to geological modelling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11184, https://doi.org/10.5194/egusphere-egu22-11184, 2022.

11:20–11:25
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EGU22-11395
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ECS
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Highlight
Luís Lopes et al.

A multi-disciplinary team – the ROBOMINERS consortium – is creating a robot-miner for the future exploitation of difficult to access deposits. The approach builds on using robotics-related capacities for the mining sector. In particular, the ROBOMINERS vision foresees the use of a modular and reconfigurable robot in a mining setting where activities are nearly invisible. Mining will be more socio-environmentally viable, thus contributing to a more safe and sustainable supply of mineral raw materials fostered by the EU Raw Materials policies. When compared to current mining methods, the ROBOMINERS approach aims at: no presence of people in the mine, less mining waste produced and mining infrastructure needed, less investment, the possibility to explore currently uneconomic resources and development of new underground small-sized mines.

In the past two years, work focused on studying and designing enabling technologies, robot components and capabilities. The next steps will include integration of different software and hardware components leading to the development of the first robotic prototype (December 2022). Critical aspects of previous studies included 1) biological inspiration, 2) perception and localisation tools, 3) robot's behaviour, navigation and control, 4) actuation methods, 5) modularity, 6) autonomy and resilience, and 7) the selective mining ability, including development of ore perception and specialized production tools. Knowledge and technology transfer from these sub-fields to the robot-miner concept were possible thanks to collaborative work developed by the different mining and robotics teams in the laboratory and online, even during the COVID-19 times.

At the same time, the vision of a new mining robotic "ecosystem" is being developed: 1) computer models and simulations, 2) data management and visualisation systems, 3) rock mechanical and geotechnical characterisation, 4) analysing ground/rock support methods, bulk transportation methods, backfilling types and mining methods, and 5) sketching  upstream and downstream mining industry analogues for the ROBOMINERS concept.

Merging of robotics and geoscientific know-how for the purpose of creating test environments (simulated and real), construction of scale models (actual and virtual), iterative development and testing key robotic functions, together with the creation of a pool of deposits that could become viable targets for future extraction, and economical studies, back up the implementation capacity of the technology.

Thanks to the integration of the previous mentioned aspects, the mining machine will be able to perform autonomous selective ore extraction. The prototype will be tested at targeted areas representatives, including abandoned and/or operating mines, small but high-grade mineral deposits, unexplored/explored non-economic occurrences and ultra-depth, not easily accessible environments. Possible current candidates for testing purposes include mines in Estonia, Slovenia or Belgium. The trials are scheduled for 2023 and will provide a first case for the operability of this new mining machine and concept.

ROBOMINERS aims at delivering a proof of concept for the feasibility of this technology line at Technology Readiness Level 4, being validated in the lab and in the test mine locations. With future-proof improvements to the technology (deriving from roadmapping) it could enable the EU to access mineral raw materials from domestic sources that are otherwise inaccessible or uneconomic.

How to cite: Lopes, L., Rossi, C., Bodo, B., Stasi, G., Burlet, C., Henley, S., Correia, V., Pinkse, T., Kot-Niewiadomska, A., Aaltonen, J., Berner, M., Cristo, N., Hartai, É., Žibret, G., Horvath, J., and Ristolainen, A.: Robot-miners for a new mining future, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11395, https://doi.org/10.5194/egusphere-egu22-11395, 2022.

11:25–11:30
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EGU22-12566
Christian Burlet et al.

ROBOMINERS (Bio-Inspired, Modular and Reconfigurable Robot Miners, Grant Agreement No. 820971, http://www.robominers.eu) is an European project funded by the European Commission's Horizon 2020 Framework Programme. The project brings roboticists and geoscientists together to explore new mining and sensing technologies and demonstrate a small robot-miner prototype designed to exploit unconventional and uneconomical mineral deposits (technology readiness level 4 to 5). This approach could change the current mining paradigms dictated by larger existing machines, while reducing mining waste and environmental footprint (Lopez and al. 2020).

One of the key function of ROBOMINERS is the “selective real-time mining”, in other words the ability for the miner to choose an optimal progression path while mining in a particular orebody geometry (inspired by the petroleum industry geo-steering technique). This will be done by a continuous monitoring of the surrounding rock properties (hardness, abrasivity, electrochemistry, thermal conductivity, 3D electrical/induced polarization tomography), and by a “digestive mineralogy” unit, performing on-board mineralogical/geochemical diagnostics of the extracted material.

After an extensive review and tests on existing sensing techniques, the consortium selected a few sensing methods, based on the considered environment (underground gallery drilling, mud/slurry-filled environment with very limited to no visibility) and the opportunity to test proven techniques as well as original methods that can be distributed on and in the miner body.

Mineralogical sensor prototypes on ROBOMINERS are articulated on 3 techniques : multi/hyperspectral reflectance, UV fluorescence and Laser-induced breakdown spectrometry (LIBS). The first two techniques are well established and easily integratable on a robotic platform. ROBOMINERS will demonstrate how miniatirization/distribution of these sensors on and in the robot can yield fast diagnostics from the excavated material. LIBS is a very interesting atomic emission technique for real-time monitoring of slurries with fast multi-element detection and low detection limits, even on light elements. It has been already used as a competitive approach to monitor slurries using flow cells in mining (Khajehzadeh et al., 2017) and inside molten metals in metallurgy applications (Moreau et al., 2018). LIBS typically achieve fast and sensitive analysis in a few micro- to milli- seconds. While true quantitative measurements remain a challenge outside a controlled lab environment, qualitative and semi-quantitative measurements are possible and is very relevant for ROBOMINERS selective mining application.

The work presented here deals with the conceptualization of the spectrometer suite. Tested slurry analogs include mixtures of lead-zinc sulfides, copper cobalt oxides, phosphorites and oil shales. Once an fixed instrumental setup is selected, the next development steps include retrofitting for testing in an industrial scale slurry circulation system at the K-UTEC facilities (Sondershausen, Germany) and, after validation of all components, integration on the ROBOMINERS prototype for the field demonstrations planned in 2023.

References.

Lopes, B. Bodo, C. Rossi, S. Henley, G. Žibret, A. Kot-Niewiadomska, V. Correia, Advances in Geosciences, Volume 54, 2020, 99–108

Khajehzadeh, O. Haavisto, L. Koresaar, , Minerals Engineering, Volume 113, 2017, pp 83-94

Moreau, A. Hamel, P. Bouchard, and M. Sabsabi, , CIM Journal, Volume 9, No. 2, 2018

How to cite: Burlet, C., Stasi, G., Pinkse, T., Piho, L., and Ristolainen, A.: The ROBOMINERS mineralogical sensors: spectrometer prototypes for autonomous in-stream, in-slurry geochemical diagnostics. , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12566, https://doi.org/10.5194/egusphere-egu22-12566, 2022.

11:30–11:35
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EGU22-5726
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ECS
Walid Remmas et al.

Different types of terrains can be encountered in mining environments, varying from hard rock bottom to mud, including gravel and sand. In our research we are investigating the usage of Archimedean screw actuators for locomotion in mining environments, as they are mechanically robust and can work on various substrates. The limitations on using screw locomotion in autonomous robotics include its inherent property of slippage that varies depending on the type of terrain. Moreover, the dynamic model of an Archimedean screw depends on variables such as shear stress or sinkage, which are difficult to measure with the onboard sensors. To accurately model and later control such robots, we focus on the dynamic modelling of the screw-ground interaction based on real experiments. In this work, we approximate the dynamics of an Archimedean screw to those of different tire models available in the literature. The proposed models are used to; (1) Simulate the ground-screw interaction with several types of grounds. (2) Estimate the robot pose based on odometry. (3) Design adaptive controllers able to control the robot in grounds with varying properties. We validate the proposed dynamic models based on experimental force measurements, and we evaluate the accuracy of the derived odometry models based on visually measured ground truth data.

How to cite: Remmas, W., Gkliva, R., and Ristolainen, A.: Dynamic modelling of a screw actuator for improved locomotion control on various terrains, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5726, https://doi.org/10.5194/egusphere-egu22-5726, 2022.

11:35–11:40
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EGU22-2716
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ECS
Esther Aguado et al.

Robustness and resilience are crucial requirements for robots operating in unstructured and hazardous environments, such as the systems developed within the ROBOMINERS project. The miner robot shall handle, at least to some extent, the disturbances it may suffer; especially given the reduced possibility of human intervention in deep, small, and difficult-to-access deposits. In ROBOMINERS, we use self-awareness mechanisms to enhance robot miner autonomy. This capability enables the robot to be aware of the state of all its components (both hardware and software) and to what extent they are complying with their functions. Moreover, self-aware systems can reason about the run-time state and detect the causes of system failures. Depending on the specific characteristics of the affected robot, failure management mechanisms can be implemented at different levels. Robots can be designed to change their physical or software configuration, change the functions of some of their components, or adapt their behaviour to match mission needs. Our approach uses the knowledge of the systems engineer through machine-readable metamodels to provide the robot with information about the mission, the environment, and itself. These formal models allow the system to reason about its run-time situation. The ROBOMINERS resilience-augmenting solution is based on deep modeling of the functional architecture of the autonomous robot in combination with runtime reasoning. The reflective reasoning of the robot allows for both self-diagnosis and reconfiguration during mining operations. One of the main advantages of this knowledge-centric approach is the explicit definition, allocation, and linkage of system requirements, design decisions, system realization, and run-time information. This approach can transparently use robot structural and functional redundancy to ensure mission satisfaction, even in the presence of faults. Moreover, the use of several meta-models and ontologies allows the segmentation of information into different domains and levels of abstraction. These independent assets can then be re-targeted and adapted to a variety of systems, sub-systems, and contexts to improve asset reuse.

How to cite: Aguado, E., Sanz, R., and Rossi, C.: Self-awareness for robust miner robot autonomy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2716, https://doi.org/10.5194/egusphere-egu22-2716, 2022.

11:40–11:45
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EGU22-2740
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ECS
Virgilio Gomez and Miguel Hernando

In the framework of the ROBOMINERS project, we are developing a set of modular collaborative robots that can perform mining operations. The purpose of this work is to face the challenge of taking modular robots out of the academic context and to provide robotic miners with the needed resilience which will be based on four pillars: redundancy, physical reconfiguration, adaptive behavior, and system reconfiguration. To do so, we are working on a scaled prototype based on a highly configurable modular robot that allows the connection between several autonomous robots (modules) and functional submodules (e.g., sensors, mining tools, locomotion devices) where resilience, energy sharing, self-reconfigurability, modularity, and self-awareness capabilities will be tested both in simulation and real-world scenarios. For each robot module, a lightweight and compact main structure is composed of three compartments and three docking ports for each of the robot legs. In each of these compartments most of the electronic components that allow the proper functioning of the robot are located, while in the legs a 4 degrees of freedom closed chain parallel mechanism powered by multi-turn servomotors is responsible of moving the interchangeable end effectors (screws, continuous tracks, legs) designed with a common coupling interface. In addition, an innovative soft telescopic robot arm (Patent pending) is placed at the front of the robot module and allows the coupling of another robot, sensing or actuation module. In parallel to the robot prototype development, a digital twin is being developed in order to test and improve different configurations, localization, mine mapping, and control algorithms techniques before deploying them in the robot.

How to cite: Gomez, V. and Hernando, M.: Modular collaborative resilient robots for mining operations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2740, https://doi.org/10.5194/egusphere-egu22-2740, 2022.

11:45–11:50
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EGU22-10484
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ECS
Emanuele Tucci and Rudi Ruggeri

In Europe there are a lot of abandoned mines that could be reopened with the use of innovative techniques; this is one of the aims of the ROBOMINERS project.

The use of the mining robots will especially be relevant for mineral deposits that are small or difficult to access.

Knowledge of type and dimensions of this mines is fundamental aiming to design and plan on-site tests of this robot.

In this article are explained the selection criteria of some mines in Italy among all the abandoned mines available at national level that could be investigated with robot miner.

In Italy there are about 3000 abandoned mining sites. Among these, eleven sites distributed throughout the national territory were selected.

Starting from a national public database containing all the abandoned mining sites and using an ad hoc KPI-matrix, some pilot sites were selected that met the required features.

The selection was carried out, according to the objectives of the project, preferring mining sites in urban areas, located at great depths or considered not economically relevant by traditional mining.

Among these, preference was given to metal-bearing ore deposits that could be better excavated with robot.

In order to characterize the selected sites, the following data have been collected for every site:

  • Geographic informations;
  • Historic time range of exploration ;
  • Deposit type;
  • Commodities available;
  • Main host rock.

Data collection was performed starting from the national database and subsequently integrating the informations with further data from bibliographic sources.

Data collection for the selected mines is of primary importance because the type of deposits can affect the correct functioning of the robot.

In order to design robot tools correctly is therefore essential to know in advance the geographic and geological features of the mine in order to carry out on-site tests.

How to cite: Tucci, E. and Ruggeri, R.: Robotics for raw material: the importance of data collection in the design of the appropriate equipments for exploring abandoned mines , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10484, https://doi.org/10.5194/egusphere-egu22-10484, 2022.