ERE4.4

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.

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.

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.

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.

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.

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.