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By Qiang Ji, Richard L. Sanford

A laboratory-scale microcomputer-based autonomous excavation system has been developed that moves a pile of fragmented rock to a designated location using a shovel attachment on a robot arm. Navigation and excavation sub- systems interactively direct a robot excavator to move the shovel to the rock pile, intelligently load its bucket using a video camera, and dump the rocks to a designated area. Although research was conducted on a laboratory scale model, the system can serve as the basis for further work on a full-scale, robust excavation system.

Jan 1, 1993

By R Price, B H. Miller

"The rise of autonomous haul truck fleets may have been initially driven by the rapid escalation of the cost of equipment operators, but the actual improvements reported by the major iron ore miners far exceeds the simple reduction in workforce. Utilising cost models developed for feasibility studies and mining contract modelling, the limits of cost improvements due to simple labour reductions are compared to those achieved by the early autonomous fleet adopters and through traditional process improvement techniques such as fleet class upgrades or hot-seating of equipment. As autonomous fleet implementation matures, it is likely that the gains seen by current adopters will seem minor compared to the limits generally governed by the limit of equipment utilisation. This ultimate limit is explored through time utilisation models, which were also developed for use in feasibility studies and mining contract modelling.Citation:Miller, B H and Price, R, 2016. Autonomous Haul System Savings - It's Not about Less Truckies but Better Truckies!, in Proceedings Ninth AusIMM Open Pit Operators' Conference 2016, pp 2–10 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 15, 2016

By William Gleason

"Komatsu has been on the cutting edge of autonomous mining for the past 30 years and the company remains at the forefront of the technology that is changing from concept to a full-fledged reality.In 1996, Komatsu America Corp. (KAC) purchased a controlling interest in Modular Mining Systems; a move that enabled both companies to expand their product lines. It was through this effort that Modular helped to deploy the first autonomous haulage system (AHS) — the Komatsu FrontRunner system. In January 2008, the first commercial implementation of the FrontRunner system occurred at Codelco’s copper mine in Chile. Since then, Komatsu has put more than 130 haul trucks into seven sites on three continents to haul copper, iron ore and oil sands and its equipment has moved 1.8 Gt (2 billion st) of material with zero lost time incidents attributed to Komatsu’s autonomous trucks. The 1.8 Gt (2 billion st) is the most of any commercial mining autonomous haulage system and there is more to come. In November, Komatsu announced that an additional 150 autonomous haul trucks will be deployed to the Canadian oil sands over the next seven years.Komatsu has continued to pioneer autonomous haulage development, creating a range of autonomous options that includes semiautonomous and teleremote options all the way up to a haulage vehicle that stole the show at the world’s largest mining equipment trade show.In 2016, Komatsu advanced the concept of driverless haul truck to a new level when it displayed its Innovative Autonomous Haulage Vehicle (IAHV) at MINExpo in Las Vegas, NV. The concept vehicle is the first mega class haul truck designed to be fully autonomous, without an operators’ cab included on the vehicle.Unlike other haul trucks, the vehicle was developed exclusively as an unmanned vehicle designed to maximize the advantages of unmanned operation. Among other advancements, it was designed to distribute equal load to the four wheels, both when the vehicle is loaded and unloaded, and adopt four-wheel drive, retarder and steering. Komatsu said it is aiming for high-performance shuttling of this vehicle in both forward and reverse travel directions, thereby eliminating the need for K-turns at loading and unloading sites."

Dec 1, 2018

By A G. L Pratt

The record for autonomous haulage systems in underground mining over the past 15 years is patchy, marked by many trials and attempts, with few examples of sustained operation until relatively recently. The drivers for the introduction of this technology are variously listed as the occupational health and safety of people working in underground mines, reductions in direct operating costs and increases in productivity.This paper reviews the trends for operation of semi-autonomous and autonomous haulage achieved by underground mines and the defining features of the technology currently available. The inclusion of autonomous haulage as part of mining studies and the fundamental conditions for its successful application is discussed in the paper. The components required for an autonomous underground haulage system are outlined and the key aspects that need to support its effective implementation are examined together with their associated opportunities and risks. Special note is made of the critical role people, including executive leadership, have in ensuring the successful introduction of this technology.The paper draws on published case study material, equipment supplier information and research findings. The aim is to offer a high-level insight into what is required for the application of autonomous haulage technology and its successful implementation in underground mines.CITATION:Pratt, A G L, 2016. Autonomous haulage systems in underground mining – what is required for success? In Proceedings International Mine Management Conferenc, pp 35-52 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jun 22, 2016

By R Price

"Technology has the potential to revolutionise industries, work functions or businesses. One such technology in the mining industry is the deployment of automation in a modern autonomous haulage system (AHS). This paper examines a theoretical application of an AHS in a medium scale mine site. The cost-benefit commercial argument is particularly compelling, and the paper outlines metrics and future issues for consideration.Citation:Price, R, 2016. Autonomous Haulage Systems - the Business Case, in Proceedings Ninth AusIMM Open Pit Operators' Conference 2016, pp 11–17 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 15, 2016

By E Nettleton, A Melkumyan

Identification of geology and mineralogy in situ on the mine face is essential if mining processes are to be automated for tasks such as exploration, grading and reconciliation. Hyperspectral data acquired from field based platforms are ideally suited to these tasks, and are set to revolutionise the automated mining industry. Changing conditions of solar illumination introduces variation into the hyperspectral signal, which is unrelated to geology, complicating analysis of data. Thus, algorithms must be able to deal with changing illumination conditions and operate in a consistent and probabilistic framework, which is a prerequisite for data fusion with other sensors. To produce accurate classification maps of geology, algorithms must be developed that are effective under different conditions of illumination.The efficacy of three algorithms to classify hyperspectral data for geology mapping on vertical mine faces was assessed. These algorithms are the spectral angle mapper (SAM), and two machine learning methods operating within a fully probabilistic Gaussian process (GP) framework û the squared exponential (SE) and the observation angle dependent (OAD) covariance functions (kernel). Two experiments were done to assess the classification performances. The first determined the performance of the algorithms by classifying hyperspectral data of known rock samples acquired using a field spectrometer. The GP-based methods had a similar performance, with average accuracies and F-scores of 97 and 88 per cent (GP-OAD) and 95 and 81 per cent (GP-SE), respectively. Average accuracies and F-scores for SAM were significantly smaller (80 and 40 per cent respectively). The algorithms were then applied to hyperspectral imagery acquired from a vertical mine face at different times of the day, under very different conditions of illumination. In contrast with experiment 1, SAM and GP-OAD gave results which were superior to GP-SE and were entirely consistent with geology mapped in the field. Compared with SAM, GP-OAD was less sensitive to the effects of shadow and produced classified images that were less noisy. The GP-SE method failed to delineate even gross changes in geology on the mine face. These results show conclusively that in order to use independent training data (spectral library) to classify imagery acquired under a different set of conditions, algorithms must take into account variability in incident illumination. Measures based on spectral distance are inferior to those based on spectral angle.The OAD kernel within the GP framework (GP-OAD) is superior to the other two methods because:it provides a probabilistic framework;it is insensitive to variability in illumination within and between imagery acquired under different conditions; andit can identify and map geological zones on a vertical mine face using an independent spectral library, without a priori knowledge.

Sep 26, 2011

By Mahmoud Ahmed

Three-dimensional models are in increasing demand for design, maintenance, operations and construction project management. Point-clouds are the main output of automatic data collection using laser-scanners and photogrammetric technologies. Pipe-works may comprise 50% of the value of important construction projects such as industrial and research facilities. We have developed a practical and cost-effective approach, based on the Hough-transform and judicious use of domain constraints, to find, recognize and reconstruct/model 3D pipes within point-clouds fully automatically. The developed approach allows pipe growing within a 3D point-cloud. It samples the point-cloud to sequential orthogonal slices, detects pipe?s cross-sections within sampled slices and grows the pipe along the cross sections centerlines. It is validated using laser-scanner data from construction of the Engineering-VI building on the University of Waterloo (UW) campus. The system works on a typical laptop. Recognition and localization results are within a few millimeters. The presented approach opens the gate to broad applications of pipe-network modeling.

Aug 1, 2013

By V. Polotski

Initiated and financed by the Canadian Space Agency, Micro-Rover Platform with Tooling Arm (MRPTA) project has targeted the development of a robotic system with remote control and autonomous navigation capabilities for testing a large variety of analogous planetary missions. Although developed for planetary exploration, the navigation system is highly suitable to such areas of application as construction and in mining. Autonomous navigation system is capable of moving the platform to predefined position(s)/orientation(s) while continuously mapping the terrain, assessing its traversability and choosing most appropriate path to follow. Designed according to the JAUS (Joint Architecture for Unmanned Systems) framework, the system consists of several interconnected components. Following components are of particular importance: Pose Estimator (PE), Terrain Evaluator (TE), Map Manager (MP), and Path Planner (PP). The PE uses a minimal sensor configuration consisting of an azimuth gyroscope, inclinometer and wheel odometry. The PE provides reliable pose estimates in spite of pronounced slippage by employing extensive use of fused data. The TE uses a nodding laser scanner which continuously sweeps the area in front of the platform and constructs a ?traversability grid? (TG) of the surrounding area. Multiple TGs are constantly constructed along the platform motion path are combined into a terrain map maintained by the MP. The PP uses this map to compute a motion path Obtained path is executed by the motion controller and remains current until it is contradicted by the most recent map ? at which point it is re-planned. This paper addresses integration issues, lessons learned and also outlines the possible applications for construction and mining.

Aug 1, 2013

By Gary M. Bone

The autonomous control of vehicles interacting forcibly with their environment, such as dozers and tractors, is an unsolved and challenging problem. Forces and motions are inherently coupled between the tool and the means of vehicle propulsion. Furthermore, they are often operated within uncertain and unstructured environments, such as those encountered in underground mining. There is a growing industrial interest in the development of robotic vehicles to improve productivity, efficiency and safety in mining and construction. This paper focuses on the modeling and control of autonomous robotic dozing for a material removal operation. A dozing process model has been developed based on observations of both a fullscale dozer and a scaled-down version. The model characterizes the dynamic interactions between the blade position, material accumulation on the blade, material distribution in the environment, and the motions of the dozer. The dozing control objective is to remove the loose material as rapidly as possible by driving forwards at full power while automatically raising/lowering the blade in response to sensor measurements. Two different controllers were developed to meet this objective. The first controller is based on a set of heuristic rules, and the second is an optimal controller based on the dynamic model. An instrumented scaled-down robotic dozer and dozing environment, designed to emulate the full-scale operation, are used to implement the controllers and compare their performances over multiple dozing passes. Experimental results are presented showing that the model-based controller increased the material removal rate by 33% compared to the rule-based controller. Lastly, technologies for full-scale implementation are discussed, followed by proposed future work and conclusions.

Aug 1, 2013

By Olav Rune Godø

We present a science-based infrastructure for continuous online monitoring of the ocean interior including benthic, pelagic and the demersal habitats. It will reduce expensive ship based monitoring costs associated to offshore and coastal industries. We combine established Norwegian cabled observatory technology and German mobile seafloor robotic technology and thus supports the transition from local to spatial ecological monitoring. Both technologies are developed through science - industry partnerships. In Norway, the Institute of Marine Research has worked with Statoil ASA and Metas AS to install and operate the LoVe cabled observatory. The German technology is developed by iSeaMC and Kraken Robotik, two spinoff enterprises of the Helmholtz Alliance “ROBEX”. GEOMAR has recently developed a lander-based deep-sea crawler that operates autonomously based on the original iSeaMC crawler system using the Kraken Autonomy. Combined with the Spanish image processing and modelling expertise in the SME Deusto Sistemas SA the consortium hosts the capacity to establishing a complete semi-autonomous real-time monitoring system. Merging of existing technologies into one operational autonomous product through the unique competence of partners’ support international ambitions (e.g. Blue Growth, GES, Horizon 2020), and renew monitoring that assess impact of human use of the marine environment. The consortium has been invited to contribute to technology/science programs for the Yellow Sea and for European mining and offshore decommissioning industries, which demonstrates the leading role of our consortium. The project is funded through the EU Martera program and started in June this year.

Jan 1, 2018

By J. Nsasi Bakambu

This paper describes an autonomous platform for exploration and navigation within networks of tunnels, as those typically found in underground mines and caves. In the exploration mode, a remotely located supervisor instructs the platform to move through successive sections of the network, gathering range data that is, then, concatenated into 2D/3D survey maps of the environment. In the navigation mode, the supervisor specifies high-level missions on the previously acquired survey maps. A motion planner, then, translates each mission into a set of consecutive navigation actions, separated by natural landmarks. Mission execution consists of detecting landmarks, self-localizing and performing the planned navigation actions.

May 1, 2003

By M. P. Tibbs

An autonomous spray application system for a structural Thin Spray-on Liner (TSL) is targeting productivity and safety on underground coal continuous miners by potentially replacing steel mesh, and has broader applications as surface support and confinement in metalliferous mines and civil tunnelling. Polymer chemistry, coating formulation, geotechnical assessment and the autonomous application system are being developed as an integrated package as part of a continuous miner automation project. This polymer-based TSL system has significant benefits over conventional steel mesh skin confinement, including being more automatable by spray application, providing complete skin confinement and active skin reinforcement as a fast-curing composite with substrate, and having effective fire retardant properties for the polymer and the underlying coal. This paper presents a preliminary assessment of distance sensing systems that would enable autonomous adaptation of a nominal spray path to spray a consistent thickness of TSL (nominally 5mm) over an irregular topography. Two alternate sensors are assessed; firstly, a 2D LIDAR mounted on a linear axis is used to produce a 3D surface profile for generating an adapted spray path; and secondly, a robot end-effector-mounted ultrasonic sensor is assessed for wall following techniques to dynamically adapt a nominal spray path.

Aug 1, 2013

By V. Polotski

This paper describes a platform for autonomous operations in open pit mining environments. A typical operation consists of: (1) Initial locomotion toward a remote worksite; (2) Pattern navigation within the worksite; (3) Worksite manipulations and (4) Return locomotion to the start location. The platform includes precise localization module based upon the fusion of odometry, inertial and GPS measurements. The guidance module starts from a set of way points specified on a GPSreferenced map and plans and executes the mission. A typical mission includes specified manipulative tasks along the navigation path. During motion execution the on-board system continuously verifies that the path is free of obstacles and that the hardware is not overloaded. Momentary losses of external positioning measurements does not adversely affect the performance, owing to the redundancy in position and heading estimation. A rehearsal phase follows planning and allows the supervisor to examine the kinematic behavior of the platform during motion, thus reducing the need for continuous monitoring during execution and providing protection against potential losses of communication.

May 1, 2003

By Juliana Parreira

The past decade shows record increases in global demand for minerals and metals with an accompanying significant decline in skilled personnel. Mining companies today are examining new technologies to maximize production, reduce costs, and create safer work conditions to deal with this shortfall. Automation of an open-pit mine haulage system is receiving attention as a beneficial option since it provides more consistent and efficient operation of mining equipment, it removes workers from potential danger, it reduces fuel consumption significantly reducing greenhouse gas (GHG) emissions, and it can help optimize vehicle repairs and equipment replacement because of more-predictable and better-controlled maintenance. This paper describes a simulation model under development using the EXTENDSIM software package to examine scale-up issues and constraints of autonomous vehicles in comparison to a manual system. The software can predict and manage Key Performance Indicators (KPIs), such as productivity, safety, cost, equipment failures, fuel consumption, and tire wear under different road and load conditions. By breaking-down the various systems of an autonomous haulage truck and simulating the overall haulage system using a Monte Carlo approach, the model becomes a flexible and powerful tool to use within a mining organization. Direct comparison to a manuallyrun system can be done to show the cost and operational benefits of automation. Project managers can use this tool to guide decision-making about possible application of autonomous haulage trucks at a specific mine site.

May 1, 2010

By Juliana Parreira

The past decade shows record increases in global demand for minerals and metals with an accompanying significant decline in skilled personnel. Mining companies today are examining new technologies to maximize production, reduce costs, and create safer work conditions to deal with this shortfall. Automation of an open-pit mine haulage system is receiving attention as a beneficial option since it provides more consistent and efficient operation of mining equipment, it removes workers from potential danger, it reduces fuel consumption significantly reducing greenhouse gas (GHG) emissions, and it can help optimize vehicle repairs and equipment replacement because of more-predictable and better-controlled maintenance. This paper describes a simulation model under development using the EXTENDSIM software package to examine scale-up issues and constraints of autonomous vehicles in comparison to a manual system. The software can predict and manage Key Performance Indicators (KPIs), such as productivity, safety, cost, equipment failures, fuel consumption, and tire wear under different road and load conditions. By breaking-down the various systems of an autonomous haulage truck and simulating the overall haulage system using a Monte Carlo approach, the model becomes a flexible and powerful tool to use within a mining organization. Direct comparison to a manuallyrun system can be done to show the cost and operational benefits of automation. Project managers can use this tool to guide decision-making about possible application of autonomous haulage trucks at a specific mine site.

May 1, 2010

By Frank Johnson

Between 2000 and 2006, platinum production from the South African mines expanded from 206 to 307 tonnes (DME, 2007). Most of this additional metal has come from traditional ?narrow? reef stoping, generally taken to mean stoping in working places with a mining height of less than 1,5 metres. It is well known that narrow-reef stoping presents safety, technological and ergonometric challenges (Pickering, 2007). In planning for the expansion, which is still continuing, the platinum mining industry realized that it could not simply continue with traditional methods of narrow-reef mining. Particular issues identified were: ? High labour-intensivity: caused by use of hand-held rock drilling machines, labour-intensive support installation and stope cleaning; ? Dependence on rock-drillers: this occupation is no longer an attractive one and there is a reducing flow of new recruits; ? Safety: most of the work is performed close to the new faces, where there is the greatest exposure to the hazards of falling rock.

Jan 1, 2008

By Huguez Ames, Ulisses Rogerio Da Silva

El presente estudio desarrollado por Investigación y Desarrollo de Procesos Químicos y Metalúrgicos en Tecsup – Lima, aliado con Stockholm Mining ha aplicado tratamientos en base a la oxidación de especies y así seleccionar el método más viable técnica y económicamente para lograr remover el cianuro total en dicha agua clarificada hasta por debajo de 1,0 mg/L para ser recirculada para diluir a la pulpa inicial, lo que repercutirá dentro de la propuesta de cambios en obtener una pulpa detoxificada en cianuro que pueda usarse como relleno en pasta para labores mineras.

Sep 1, 2017

By W. G. Davenport

Chapter 6 examined autotherrnal smelting of pure chalcopyrite concentrate with 50 mass% O2-50 mass% N2 blast. It showed that production of 1500 K products with this blast requires: 181 kg of pure oxygen 337 kg of air per tonne, of concentrate. It also showed that the grade of matte produced with this blast is 58% Cu. This chapter re-examines autotherrnal smelting by asking: (A) WHAT COMPOSITION BLAST (MASS% 02 -MASS% N2), 298 K, WILL AUTOTHERMALL Y PRODUCE 65% Cu MATTE 1500 K FROM PURE CHALCOPYRITE CONCENTRATE? (B) HOW MUCH PURE OXYGEN AND AIR WILL BE NEEDED? These are important questions for flash? smelting because furnace operators usually want to send specific composition matte to converting. This chapter shows how a specified matte can be produced autotherrnally with 298 K oxygen-air blast. Later chapters show how it can be done with hot blast, hydrocarbon combustion, different concentrates etc.

Jan 1, 2001

By Peter Kowalczyk

Magnetic surveying and map production is well established in land based mineral exploration. Magnetic data are routinely used to map geology in covered terrains, to estimate the depth to basement in overburden covered areas, and to identify altered zones, mineralization, bedding attitudes, and fault networks both regionally in map view, and through interpretation to depth in 3D. Best practices for magnetic survey methods and magnetic interpretation exist in all areas of land based geophysical survey methodology - mineral exploration, hydrocarbon exploration, geological mapping, archeological mapping, and in military applications. At sea, and particularly under the sea, sophisticated magnetic surveys are being done, but it can be argued that these are primarily in niche applications, for example in naval mine-hunting and UXO applications. Magnetic data is not normally collected on AUVs and ROVs conducting mapping and hydrographic survey operations. OFG has developed physical and mathematical calibration routines so that magnetometers can be mounted directly onto AUV and ROV bodies where significant heading errors and magnetic gradients exist. This simplifies the routine deployment of magnetometers on AUVs and ROVs for mineral exploration and other magnetic survey applications without the need for towed bodies, ungainly mounting frames, or degaussing of the survey equipment. Our experience shows that magnetometers can be operated routinely on AUVs and ROVs and that with careful processing useful maps can be produced routinely at low cost.

Sep 1, 2014

By J. W. Jamieson, J. Escartin, A. Steinführer

Evidence of hydrothermal activity on two Mid-Atlantic Ridge oceanic core complexes (OCCs), at 13º30’N (the Semyenov 1 through 5 fields) and 13º20’N (the Irinovskoe field) (Fig. 1) were studied using CTD casts, dredges, TV-grabs and SP surveys during previous research cruises to these sites (Cherkashev et al., 2013, McLeod et al., 2009). In late 2013, the Semyenov and Irinovskoe fields were revisited, this time with an autonomous underwater vehicle (AUV) and a remotely-operated vehicle (ROV) during the ODEMAR (Oceanic Detachments at the Mid-Atlantic Ridge) cruise on the French research vessel "Pourqoui Pas ?". Here we present the first visual surveys and direct sampling of these vent sites. High-resolution (~2 m) bathymetry of the 13º30’N and 13º20’N OCCs and their associated vent fields was collected during 4 and 5 AUV dives, respectively, using the GEOMAR AUV "ABYSS" (Fig. 1). These surveys were flown between 70 m and 100 m above the seafloor, typically at a constant height to reduce vehicle movement. The AUV was equipped with Eh, turbidity, and temperature sensors in order to detect active hydrothermal venting. Over twenty ROV dives with the ROV "VICTOR 6000" allowed for the visual exploration of the OCCs and collection of 59 hydrothermal rock samples from the vent fields. As a result of these exploration activities, the first robust estimates of the size of the deposits, and the geologic controls on sulfide accumulation at these sites can be determined.

Sep 1, 2014