DEVELOPMENT OF AN AUTONOMOUS NAVIGATION SYSTEM FOR THE SHUTTLE CAR IN UNDERGROUND ROOM & PILLAR COAL MINES

In recent years, autonomous solutions in the multi-disciplinary field of the mining engineering have been an extremely popular applied research topic. The growing demand for mineral supplies combined with the steady decline in the available surface reserves has driven the mining industry to mine deeper underground deposits. These deposits are difficult to access, and the environment may be hazardous to mine personnel (e.g., increased heat, difficult ventilation conditions, etc.). Moreover, current mining methods expose the miners to numerous occupational hazards such as working in the proximity of heavy mining equipment, possible roof falls, as well as noise and dust. As a result, the mining industry, in its efforts to modernize and advance its methods and techniques, is one of the many industries that has turned to autonomous systems. Vehicle automation in such complex working environments can play a critical role in improving worker safety and mine productivity. One of the most time-consuming tasks of the mining cycle is the transportation of the extracted ore from the face to the main haulage facility or to surface processing facilities. Although conveyor belts have long been the autonomous transportation means of choice, there are still many cases where a discrete transportation system is needed to transport materials from the face to the main haulage system. The current dissertation presents the development of a navigation system for an autonomous shuttle car (ASC) in underground room and pillar coal mines. By introducing autonomous shuttle cars, the operator can be relocated from the dusty, noisy, and potentially dangerous environment of the underground mine to the safer location of a control room. This dissertation focuses on the development and testing of an autonomous navigation system for an underground room and pillar coal mine. A simplified relative localization system which determines the location of the vehicle relatively to salient features derived from on-board 2D LiDAR scans was developed for a semi-autonomous laboratory-scale shuttle car prototype. This simplified relative localization system is heavily dependent on and at the same time leverages the room and pillar geometry. Instead of keeping track of a global position of the vehicle relatively to a fixed coordinates frame, the proposed custom localization technique requires information regarding only the immediate surroundings. The followed approach enables the prototype to navigate around the pillars in real-time using a deterministic Finite-State Machine which models the behavior of the vehicle in the room and pillar mine with only a few states. Also, a user centered GUI has been developed that allows for a human user to control and monitor the autonomous vehicle by implementing the proposed navigation system. Experimental tests have been conducted in a mock mine in order to evaluate the performance of the developed system. A number of different scenarios simulating common missions that a shuttle car needs to undertake in a room and pillar mine. The results show a minimum success ratio of 70%

CONCEPTS FOR DEVELOPMENT OF SHUTTLE CAR AUTONOMOUS DOCKING WITH CONTINUOUS MINER USING 3-D DEPTH CAMERA

In recent years, a great deal of work has been conducted in automating mining equipment with the goals of increasing worker health and safety and increasing mine productivity. Automating vehicles such as load-haul-dumps been successful even in underground environments where the use of global positioning systems are unavailable. This thesis addresses automating the operation of a shuttle car, specifically focusing on positioning the shuttle car under the continuous miner coal-discharge conveyor during cutting and loading operations. This task requires recognition of the target and precise control of the tramming operation because a specific orientation and distance from the coal discharge conveyor is needed to avoid coal spillage. The proposed approach uses a stereo depth camera mounted on a small-scale mockup of a shuttle car. Machine learning algorithms are applied to the camera output to identify the continuous miner coal-discharge conveyor and segment the scene into various regions such as roof, ribs, and personnel. This information is used to plan the shuttle car path to the continuous miner coal-discharge conveyor. These methods are currently applied on 1/6th scale continuous miner and shuttle car in an appropriately scaled mock mine

UAV Path Planning and Multi-Modal Localization for Mapping in a Subterranean Environment

The use of Unmanned Aerial Vehicles (UAVs), especially quadcopters, have become popular in academia and industry due to their small size and maneuverability. These UAVs can be programmed to autonomously execute missions that are usually difficult and risky for humans, such as subterranean exploration, infrastructure surveying, and even disaster response. However, inaccessible and remote environments pose a challenge in terms of navigation as they often lack access to Global Navigation Satellite System (GNSS) connections and lack features. To address these challenges, UAVs are equipped with multiple sensors to acquire different types of data. These include range, acceleration, and even images, which are fused to estimate a localization solution. The Autonomous Robotic Early Warning System for Underground Stone Mining Safety project, sponsored by Alpha Foundation, is conducted within the Statler College at West Virginia University (WVU). The project aims to map walls and pillars within a mine to analyze its structural integrity and safety. This thesis investigates the implementation of a path planning strategy to optimize the coverage of a wall and also the use of an error state Extended Kalman Filter (EKF) for sensor fusion to perform Simultaneous Localization and Mapping (SLAM). The experiments were carried out both in simulated and real world environments. In these experiments, the UAV was equipped with an Inertial Measurement Unit (IMU), laser altimeter, Ultra-Wideband (UWB) module (for ranging data), LiDAR for mapping, and an RGB-D camera to provide a Visual Odometry (VO) solution. In the simulation, the 3D reconstruction and odometry was compared to the ground truth, whereas the real experiment provided further insight into the strategy’s feasibility

A Novel Relative Navigation Control Strategy Based on Relation Space Method for Autonomous Underground Articulated Vehicles

This paper proposes a novel relative navigation control strategy based on the relation space method (RSM) for articulated underground trackless vehicles. In the RSM, a self-organizing, competitive neural network is used to identify the space around the vehicle, and the spatial geometric relationships of the identified space are used to determine the vehicle’s optimal driving direction. For driving control, the trajectories of the articulated vehicles are analyzed, and data-based steering and speed control modules are developed to reduce modeling complexity. Simulation shows that the proposed RSM can choose the correct directions for articulated vehicles in different tunnels. The effectiveness and feasibility of the resulting novel relative navigation control strategy are validated through experiments

Modelling and control of an articulated underground mining vehicle

The automation of the tramming or load, haul and dump (LHD) procedure, performed by a LHD vehicle, holds the potential to improve productivity, efficiency and safety in the mining environment. Productivity is mainly increased by longer working hours; efficiency is improved by repetitive, faultless and predictable work; and safety is improved by removing the human operator from the harsh environment. However, before the automation of the process can be addressed, a thorough understanding of the process and its duty in the overall mining method is required. Therefore, the current applicable mining methods and their areas of potential automation are given. Since the automation of the LHD vehicle is at the core of this project, its implementation in the tramming process is also justified. Also, the current underground navigation methods are given and their shortcomings are named. It is concluded that infrastructure-free navigation is the only viable solution in the ever-changing mining environment. With that in mind, the feasibility of various navigation sensors is discussed and conclusions are drawn. Both kinematic and dynamic modelling of LHD vehicles are introduced. Various forms of kinematic models are given and their underlying modelling assumptions are named. The most prominent assumptions concern the vehicle’s half-length and the inclusion of a wheel-slip factor. Dynamic modelling techniques, with a strong emphasis on tyre modelling, are also stated. In order to evaluate the modelling techniques, field tests are performed on the articulated vehicles, namely the Wright 365 LHD and the Bell 1706C loader. The test on the Wright 365 LHD gives a good impression of the harsh ergonomics under which the operator has to work. A more thorough test is performed on the Bell 1706C articulated loader. The test results are then compared to simulation results obtained from the kinematic models. Also, the above-named assumptions are tested, evaluated and discussed. A dynamic model is also simulated and discussed. Lastly, two localization and control methods are given and evaluated. The first method is an open-loop nonlinear optimal control strategy with periodic position resetting and the second method is a pathtracking controller. AFRIKAANS : Automatisering van die laai-, vervoer- en dompel- (LVD) prosedure het die potensiaal om die produktiwiteit, effektiwiteit en veiligheid van die mynbedryf te verbeter. Produktiwiteit word hoofsaaklik deur langer werksure verhoog, effektiwiteit word deur herhalende, foutlose en voorspelbare werk verbeter en veiligheid word verbeter omdat menslike operateurs uit die gevaarlike ondergrondse omgewing verwyder word. Voordat aandag aan die automatisering van die prosedure geskenk kan word, moet die prosedure en die algemene mynbedrywighede rakende die prosedure deeglik bestudeer en verstaan word. As gevolg hiervan word die huidige, toepaslike mynboumetodes hier gedokumenteer. Die implementering van ʼn gekoppelde LVD-voertuig in die LVD-prosesword ook geregverdig. Verder word die huidige metodes van ondergrondse navigasie genoem en hulle tekortkominge aangedui. Die gevolgtrekking dat infrastruktuur-vrye navigasie die enigste lewensvatbare navigasiemetode in die immer veranderende ondergrondsemynbouomgewing is, word ook gemaak. In die lig daarvan word ʼn verskeidenheid sensors genoem en bespreek. Kinematiese en dinamiese modellering van ʼn LVD-voertuig word bekendgestel. Verskeie kinematiese modelle en hulle onderliggende aannames word genoem. Die mees prominente aannames is die lengte van die gekoppelde voertuig se hoofdele en die insluiting van ʼn wielglipfaktor. Die tegnieke van dinamiese modellering, met die klem op bandmodellering, word ook gegee. Praktyktoetse op gekoppelde voertuie is ook gedoen om die verskillende modelle te evalueer. Die toets op die Wright 365-LVD bied goeie insig in die strawwe ergonomiese toestande waaronder die operateurs moet werk. ʼn Deeglike toets is op ʼn BELL 1706C- gekoppelde laaier, wat kinematies identies aan ʼn LVD-voertuig is, uitgevoer. Die bevindinge van die toets word met bogenoemde modelsimulasies vergelyk en gevolgtrekkings word gemaak. Laastens word lokalisiering en beheer van ʼn LVDvoertuig behandel. Twee beheermetodes, opelus- nie-lineêre optimale beheer met periodieke herstel en padvolgingbeheer word geëvalueer en bespreek. CopyrightDissertation (MEng)--University of Pretoria, 2012.Electrical, Electronic and Computer Engineeringunrestricte

Modelling and control of an autonomous underground mine vehicle

The mining industry is constantly under pressure to improve productivity, effciency and safety. Although an increased use of automation technology has the potential of con- tributing to improvements in all three factors mines have been relatively slow to make use of automation technology. Automation in the underground mining environment is a challenging prospect for a number of reasons not least of which being the diffculties and associated costs of installing infrastructure in this hazardous environment. The work described in this dissertation focuses on the modelling of a Load-Haul-Dump or LHD vehicle for the purpose of autonomous navigation and control. Considerable progress has been made in automating underground mining vehicles in recent years, and successful test installations have been made. There are still however a number of shortcomings in the existing autonomous underground mine vehicle navigation systems. This dissertation attempts to address some of these problems through the development of a more accurate vehicle model for an LHD vehicle incorporating some vehicle and tyre dynamics thereby potentially reducing the number of sensors and the amount of installed infrastructure necessary to implement the vehicle navigation system. Simulation results are provided for different vehicle modelling techniques and the results are compared and discussed in terms of their suitability for physical implementation in an underground mine.Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2007.Electrical, Electronic and Computer EngineeringMEngunrestricte

Mapping and Semantic Perception for Service Robotics

Para realizar una tarea, los robots deben ser capaces de ubicarse en el entorno. Si un robot no sabe dónde se encuentra, es imposible que sea capaz de desplazarse para alcanzar el objetivo de su tarea. La localización y construcción de mapas simultánea, llamado SLAM, es un problema estudiado en la literatura que ofrece una solución a este problema. El objetivo de esta tesis es desarrollar técnicas que permitan a un robot comprender el entorno mediante la incorporación de información semántica. Esta información también proporcionará una mejora en la localización y navegación de las plataformas robóticas. Además, también demostramos cómo un robot con capacidades limitadas puede construir de forma fiable y eficiente los mapas semánticos necesarios para realizar sus tareas cotidianas.El sistema de construcción de mapas presentado tiene las siguientes características: En el lado de la construcción de mapas proponemos la externalización de cálculos costosos a un servidor en nube. Además, proponemos métodos para registrar información semántica relevante con respecto a los mapas geométricos estimados. En cuanto a la reutilización de los mapas construidos, proponemos un método que combina la construcción de mapas con la navegación de un robot para explorar mejor un entorno y disponer de un mapa semántico con los objetos relevantes para una misión determinada.En primer lugar, desarrollamos un algoritmo semántico de SLAM visual que se fusiona los puntos estimados en el mapa, carentes de sentido, con objetos conocidos. Utilizamos un sistema monocular de SLAM basado en un EKF (Filtro Extendido de Kalman) centrado principalmente en la construcción de mapas geométricos compuestos únicamente por puntos o bordes; pero sin ningún significado o contenido semántico asociado. El mapa no anotado se construye utilizando sólo la información extraída de una secuencia de imágenes monoculares. La parte semántica o anotada del mapa -los objetos- se estiman utilizando la información de la secuencia de imágenes y los modelos de objetos precalculados. Como segundo paso, mejoramos el método de SLAM presentado anteriormente mediante el diseño y la implementación de un método distribuido. La optimización de mapas y el almacenamiento se realiza como un servicio en la nube, mientras que el cliente con poca necesidad de computo, se ejecuta en un equipo local ubicado en el robot y realiza el cálculo de la trayectoria de la cámara. Los ordenadores con los que está equipado el robot se liberan de la mayor parte de los cálculos y el único requisito adicional es una conexión a Internet.El siguiente paso es explotar la información semántica que somos capaces de generar para ver cómo mejorar la navegación de un robot. La contribución en esta tesis se centra en la detección 3D y en el diseño e implementación de un sistema de construcción de mapas semántico.A continuación, diseñamos e implementamos un sistema de SLAM visual capaz de funcionar con robustez en entornos poblados debido a que los robots de servicio trabajan en espacios compartidos con personas. El sistema presentado es capaz de enmascarar las zonas de imagen ocupadas por las personas, lo que aumenta la robustez, la reubicación, la precisión y la reutilización del mapa geométrico. Además, calcula la trayectoria completa de cada persona detectada con respecto al mapa global de la escena, independientemente de la ubicación de la cámara cuando la persona fue detectada.Por último, centramos nuestra investigación en aplicaciones de rescate y seguridad. Desplegamos un equipo de robots en entornos que plantean múltiples retos que implican la planificación de tareas, la planificación del movimiento, la localización y construcción de mapas, la navegación segura, la coordinación y las comunicaciones entre todos los robots. La arquitectura propuesta integra todas las funcionalidades mencionadas, asi como varios aspectos de investigación novedosos para lograr una exploración real, como son: localización basada en características semánticas-topológicas, planificación de despliegue en términos de las características semánticas aprendidas y reconocidas, y construcción de mapas.In order to perform a task, robots need to be able to locate themselves in the environment. If a robot does not know where it is, it is impossible for it to move, reach its goal and complete the task. Simultaneous Localization and Mapping, known as SLAM, is a problem extensively studied in the literature for enabling robots to locate themselves in unknown environments. The goal of this thesis is to develop and describe techniques to allow a service robot to understand the environment by incorporating semantic information. This information will also provide an improvement in the localization and navigation of robotic platforms. In addition, we also demonstrate how a simple robot can reliably and efficiently build the semantic maps needed to perform its quotidian tasks. The mapping system as built has the following features. On the map building side we propose the externalization of expensive computations to a cloud server. Additionally, we propose methods to register relevant semantic information with respect to the estimated geometrical maps. Regarding the reuse of the maps built, we propose a method that combines map building with robot navigation to better explore a room in order to obtain a semantic map with the relevant objects for a given mission. Firstly, we develop a semantic Visual SLAM algorithm that merges traditional with known objects in the estimated map. We use a monocular EKF (Extended Kalman Filter) SLAM system that has mainly been focused on producing geometric maps composed simply of points or edges but without any associated meaning or semantic content. The non-annotated map is built using only the information extracted from an image sequence. The semantic or annotated parts of the map –the objects– are estimated using the information in the image sequence and the precomputed object models. As a second step we improve the EKF SLAM presented previously by designing and implementing a visual SLAM system based on a distributed framework. The expensive map optimization and storage is allocated as a service in the Cloud, while a light camera tracking client runs on a local computer. The robot’s onboard computers are freed from most of the computation, the only extra requirement being an internet connection. The next step is to exploit the semantic information that we are able to generate to see how to improve the navigation of a robot. The contribution of this thesis is focused on 3D sensing which we use to design and implement a semantic mapping system. We then design and implement a visual SLAM system able to perform robustly in populated environments due to service robots work in environments where people are present. The system is able to mask the image regions occupied by people out of the rigid SLAM pipeline, which boosts the robustness, the relocation, the accuracy and the reusability of the geometrical map. In addition, it estimates the full trajectory of each detected person with respect to the scene global map, irrespective of the location of the moving camera at the point when the people were imaged. Finally, we focus our research on rescue and security applications. The deployment of a multirobot team in confined environments poses multiple challenges that involve task planning, motion planning, localization and mapping, safe navigation, coordination and communications among all the robots. The architecture integrates, jointly with all the above-mentioned functionalities, several novel features to achieve real exploration: localization based on semantic-topological features, deployment planning in terms of the semantic features learned and recognized, and map building.<br /