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

Stability and robustness analysis tools for marine robot localization and mapping applications

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 114-118).The aim of this analysis is to explore the fundamental stability issues of a robotic vehicle carrying out localization, mapping, and feedback control in a perturbation-filled environment. Motivated by the application of an ocean vehicle performing an autonomous ship hull inspection, a planar vehicle model performs localization using point features from a given map. Cases in which the agent must update the map are also considered. The stability of the marine robot controller and estimator duo is investigated using a pair of theorems requiring boundedness and convergence of the transition matrix Euclidean norm. These theorems yield a stability test for the feedback controller. Perturbations are then considered using a theorem on the convergence on the perturbed system transition matrix, yielding a robustness test for the estimator. Together, these tests form a set of tools which can be used in planning and evaluating the robustness of marine vehicle survey trajectories, which is demonstrated through experiment. An augmented A* kinodynamic path-planning algorithm is then implemented to search the control input space for the globally robustness-optimal survey trajectory.by Brendan J. Englot.S.M