149 resultados para Kalgoorlie Terrain
Resumo:
The John Lewis Partnership is one of Europe’s largest models of employee ownership and has been operating a form of employee involvement and participation since its formation in 1929. It is frequently held up as a model of best practice (Cathcart, 2013) and has been described as a ‘workers’ paradise’ (Stummer and Lacey, 2001). At the beginning of 2012, the Deputy Prime Minister of the UK unveiled plans to create a ‘John Lewis Economy’ (Wintour, 2012). As John Lewis is being positioned at the heart of political and media discussions in the UK about alternatives to the corporate capitalist model of enterprise, it is vital that more is known about the experience of employee involvement and participation within the organisation. This article explores the ways in which the practice of employee involvement and participation has changed in John Lewis as a result of competing employee and managerial interests. Its contribution is a contemporary exploration of participation in the John Lewis Partnership and an examination of the ways in which management and employees contested the meaning and practice of employee involvement and participation as part of a ‘democracy project’, which culminated in significant changes and degeneration of the democratic structures.
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For a planetary rover to successfully traverse across unstructured terrain autonomously, one of the major challenges is to assess its local traversability such that it can plan a trajectory to achieve its mission goals efficiently while minimising risk to the vehicle itself. This paper aims to provide a comparative study on different approaches for representing the geometry of Martian terrain for the purpose of evaluating terrain traversability. An accurate representation of the geometric properties of the terrain is essential as it can directly affect the determination of traversability for a ground vehicle. We explore current state-of-the-art techniques for terrain estimation, in particular Gaussian Processes (GP) in various forms, and discuss the suitability of each technique in the context of an unstructured Martian terrain. Furthermore, we present the limitations of regression techniques in terms of spatial correlation and continuity assumptions, and the impact on traversability analysis of a planetary rover across unstructured terrain. The analysis was performed on datasets of the Mars Yard at the Powerhouse Museum in Sydney, obtained using the onboard RGB-D camera.
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It is well recognized that many scientifically interesting sites on Mars are located in rough terrains. Therefore, to enable safe autonomous operation of a planetary rover during exploration, the ability to accurately estimate terrain traversability is critical. In particular, this estimate needs to account for terrain deformation, which significantly affects the vehicle attitude and configuration. This paper presents an approach to estimate vehicle configuration, as a measure of traversability, in deformable terrain by learning the correlation between exteroceptive and proprioceptive information in experiments. We first perform traversability estimation with rigid terrain assumptions, then correlate the output with experienced vehicle configuration and terrain deformation using a multi-task Gaussian Process (GP) framework. Experimental validation of the proposed approach was performed on a prototype planetary rover and the vehicle attitude and configuration estimate was compared with state-of-the-art techniques. We demonstrate the ability of the approach to accurately estimate traversability with uncertainty in deformable terrain.
Learned stochastic mobility prediction for planning with control uncertainty on unstructured terrain
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Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modelling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This mobility prediction model is trained using sample executions of motion primitives on representative terrain, and predicts the future outcome of control actions on similar terrain. Using Gaussian process regression allows us to exploit its inherent measure of prediction uncertainty in planning. We integrate mobility prediction into a Markov decision process framework and use dynamic programming to construct a control policy for navigation to a goal region in a terrain map built using an on-board depth sensor. We consider both rigid terrain, consisting of uneven ground, small rocks, and non-traversable rocks, and also deformable terrain. We introduce two methods for training the mobility prediction model from either proprioceptive or exteroceptive observations, and report results from nearly 300 experimental trials using a planetary rover platform in a Mars-analogue environment. Our results validate the approach and demonstrate the value of planning under uncertainty for safe and reliable navigation.
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On our first day in Kalgoorlie, a local woman in her mid-thirties tells us that ‘Kal wouldn’t exist if it wasn’t for mining and prostitution’. In the ensuing days many others would tell us the same thing. More explicitly, in the words of another local resident, ‘The town was founded on brothels. [Without them] the men wouldn’t have been happy and they wouldn’t have got as much gold.’ These two phenomena – mining and prostitution – and their seemingly natural and straightforward connection to each other are also routinely invoked in tourist and popular culture depictions of Kalgoorlie. The Lonely Planet, for example, notes that ‘historically, mineworkers would come straight to town to spend disposable income at Kalgoorlie’s infamous brothels, or at pubs staffed by “skimpies” (scantily clad female bar staff)’.
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With the increasing need to adapt to new environments, data-driven approaches have been developed to estimate terrain traversability by learning the rover’s response on the terrain based on experience. Multiple learning inputs are often used to adequately describe the various aspects of terrain traversability. In a complex learning framework, it can be difficult to identify the relevance of each learning input to the resulting estimate. This paper addresses the suitability of each learning input by systematically analyzing the impact of each input on the estimate. Sensitivity Analysis (SA) methods provide a means to measure the contribution of each learning input to the estimate variability. Using a variance-based SA method, we characterize how the prediction changes as one or more of the input changes, and also quantify the prediction uncertainty as attributed from each of the inputs in the framework of dependent inputs. We propose an approach built on Analysis of Variance (ANOVA) decomposition to examine the prediction made in a near-to-far learning framework based on multi-task GP regression. We demonstrate the approach by analyzing the impact of driving speed and terrain geometry on the prediction of the rover’s attitude and chassis configuration in a Marsanalogue terrain using our prototype rover Mawson.
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The thick package of ~2.7 Ga mafic and ultramafic lavas and intrusions preserved among the Neoarchean of the Kalgoorlie Terrene in Western Australia provides valuable insight into geological processes controlling the most prodigious episode of growth and preservation of juvenile continental crust in Earth’s history. Limited exposure of these rocks results in uncertainty about their age, physical and chemical characteristics, and stratigraphic relationships. This in turn prevents confident correlation of regional occurrences of mafic and ultramafic successions (both intrusive and extrusive) and hinders the interpretation of tectonic setting and magmatic evolution. A recent stratigraphic drilling program of the Neoarchean stratigraphy of the Agnew Greenstone Belt in Western Australia has provided continuous exposures through a c. 7 km thick sequence of mafic and ultramafic units. In this study, we present a volcanological, lithogeochemical and chronological study of the Agnew Greenstone Belt, and provide the first pre-2690 Ma regional correlation across the Kalgoorlie Terrane. The Agnew Greenstone Belt records ~30 m.y. of episodic ultramafic-mafic magmatism that includes two cycles, each defined by a komatiite that is overlain by units that become more evolved and contaminated with time. The sequence is divided into nine conformable packages, each consisting of stacked subaqueous lava flows and comagmatic intrusions, as well as two sills without associated extrusions. Lavas, with the exception of intercalations between two units, form a layer-cake stratigraphy and were likely erupted from a system of fissures tapping the same magma source. The komatiites are not contaminated by continental crust ([La/Sm]PM ~0.7) and are of the Al-undepleted Munro-type. Crustal contamination is evident in many units (Songvang Basalt, Never Can Tell Basalt, Redeemer Basalt, and Turrett Dolerite), as judged by [La/Sm]>1, negative Nb and Ti anomalies, and geochemical mixing trends towards felsic contaminants. Crystal fractionation was also significant, with early olivine and chromite (Mg#>65) followed by plagioclase and clinopyroxene removal (Mg<65), and in the most evolved case, titanomagnetite accumulation. Three new TIMS dates on granophyric zones of mafic sills and one ICP-MS date from an interflow felsic tuff are presented and used for regional stratigraphic correlation. Cycle I magmatism began at ~2720 Ma and ended ~2705 Ma, whereas cycle II began ~2705 Ma and ended at 2690.7±1.2 Ma. Regional correlations indicate the western Kalgoorlie Terrane consists of a remarkably similar stratigraphy that can be recognised at Agnew, Ora Banda and Coolgardie, whereas the eastern part of the terrane (e.g., Kambalda Domain) does not include cycle I, but correlates well with cycle II. This research supports an autochthonous model of greenstone formation, in which one large igneous province, represented by two complete cycles, is constructed on sialic crust. New stratigraphic correlations for the Kalgoorlie Terrane indicate that many units can be traced over distances >100 km, which has implications for exploration targeting for stratigraphically hosted ultramafic Ni and VMS deposits.
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Terrain traversability estimation is a fundamental requirement to ensure the safety of autonomous planetary rovers and their ability to conduct long-term missions. This paper addresses two fundamental challenges for terrain traversability estimation techniques. First, representations of terrain data, which are typically built by the rover’s onboard exteroceptive sensors, are often incomplete due to occlusions and sensor limitations. Second, during terrain traversal, the rover-terrain interaction can cause terrain deformation, which may significantly alter the difficulty of traversal. We propose a novel approach built on Gaussian process (GP) regression to learn, and consequently to predict, the rover’s attitude and chassis configuration on unstructured terrain using terrain geometry information only. First, given incomplete terrain data, we make an initial prediction under the assumption that the terrain is rigid, using a learnt kernel function. Then, we refine this initial estimate to account for the effects of potential terrain deformation, using a near-to-far learning approach based on multitask GP regression. We present an extensive experimental validation of the proposed approach on terrain that is mostly rocky and whose geometry changes as a result of loads from rover traversals. This demonstrates the ability of the proposed approach to accurately predict the rover’s attitude and configuration in partially occluded and deformable terrain.
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Data-driven approaches such as Gaussian Process (GP) regression have been used extensively in recent robotics literature to achieve estimation by learning from experience. To ensure satisfactory performance, in most cases, multiple learning inputs are required. Intuitively, adding new inputs can often contribute to better estimation accuracy, however, it may come at the cost of a new sensor, larger training dataset and/or more complex learning, some- times for limited benefits. Therefore, it is crucial to have a systematic procedure to determine the actual impact each input has on the estimation performance. To address this issue, in this paper we propose to analyse the impact of each input on the estimate using a variance-based sensitivity analysis method. We propose an approach built on Analysis of Variance (ANOVA) decomposition, which can characterise how the prediction changes as one or more of the input changes, and also quantify the prediction uncertainty as attributed from each of the inputs in the framework of dependent inputs. We apply the proposed approach to a terrain-traversability estimation method we proposed in prior work, which is based on multi-task GP regression, and we validate this implementation experimentally using a rover on a Mars-analogue terrain.
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This is the first volume to capture the essence of the burgeoning field of cultural studies in a concise and accessible manner. Other books have explored the British and North American traditions, but this is the first guide to the ideas, purposes and controversies that have shaped the subject. The author sheds new light on neglected pioneers and a clear route map through the terrain. He provides lively critical narratives on a dazzling array of key figures including, Arnold, Barrell, Bennett, Carey, Fiske, Foucault, Grossberg, Hall, Hawkes, hooks, Hoggart, Leadbeater, Lissistzky, Malevich, Marx, McLuhan, McRobbie, D Miller, T Miller, Morris, Quiller-Couch, Ross, Shaw, Urry, Williams, Wilson, Wolfe and Woolf. Hartley also examines a host of central themes in the subject including literary and political writing, publishing, civic humanism, political economy and Marxism, sociology, feminism, anthropology and the pedagogy of cultural studies.
A research framework to investigate the performance of financial incentives in construction projects
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Since 1995 the buildingSMART International Alliance for Interoperability (buildingSMART)has developed a robust standard called the Industry Foundation Classes (IFC). IFC is an object oriented data model with related file format that has facilitated the efficient exchange of data in the development of building information models (BIM). The Cooperative Research Centre for Construction Innovation has contributed to the international effort in the development of the IFC standard and specifically the reinforced concrete part of the latest IFC 2x3 release. Industry Foundation Classes have been endorsed by the International Standards Organisation as a Publicly Available Specification (PAS) under the ISO label ISO/PAS 16739. For more details, go to http://www.tc184- sc4.org/About_TC184-SC4/About_SC4_Standards/ The current IFC model covers the building itself to a useful level of detail. The next stage of development for the IFC standard is where the building meets the ground (terrain) and with civil and external works like pavements, retaining walls, bridges, tunnels etc. With the current focus in Australia on infrastructure projects over the next 20 years a logical extension to this standard was in the area of site and civil works. This proposal recognises that there is an existing body of work on the specification of road representation data. In particular, LandXML is recognised as also is TransXML in the broader context of transportation and CityGML in the common interfacing of city maps, buildings and roads. Examination of interfaces between IFC and these specifications is therefore within the scope of this project. That such interfaces can be developed has already been demonstrated in principle within the IFC for Geographic Information Systems (GIS) project. National road standards that are already in use should be carefully analysed and contacts established in order to gain from this knowledge. The Object Catalogue for the Road Transport Sector (OKSTRA) should be noted as an example. It is also noted that buildingSMART Norway has submitted a proposal
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Objective: To define characteristics of vehicle crashes occurring on rural private property in north Queensland with an exploration of associated risk factors. Design: Descriptive analysis of private property crash data collected by the Rural and Remote Road Safety Study. Setting: Rural and remote north Queensland. Participants: A total of 305 vehicle controllers aged 16 years or over hospitalised at Atherton, Cairns, Mount Isa or Townsville for at least 24 hours as a result of a vehicle crash. Main outcome measure: A structured questionnaire completed by participants covering crash details, lifestyle and demographic characteristics, driving history, medical history, alcohol and drug use and attitudes to road use. Results: Overall, 27.9% of interviewees crashed on private property, with the highest proportion of private road crashes occurring in the North West Statistical Division (45%). Risk factors shown to be associated with private property crashes included male sex, riding off-road motorcycle or all-terrain vehicle, first-time driving at that site, lack of licence for vehicle type, recreational use and not wearing a helmet or seatbelt. Conclusions: Considerable trauma results from vehicle crashes on rural private property. These crashes are not included in most crash data sets, which are limited to public road crashes. Legislation and regulations applicable to private property vehicle use are largely focused on workplace health and safety, yet work-related crashes represent a minority of private property crashes in north Queensland.
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To navigate successfully in a previously unexplored environment, a mobile robot must be able to estimate the spatial relationships of the objects of interest accurately. A Simultaneous Localization and Mapping (SLAM) sys- tem employs its sensors to build incrementally a map of its surroundings and to localize itself in the map simultaneously. The aim of this research project is to develop a SLAM system suitable for self propelled household lawnmowers. The proposed bearing-only SLAM system requires only an omnidirec- tional camera and some inexpensive landmarks. The main advantage of an omnidirectional camera is the panoramic view of all the landmarks in the scene. Placing landmarks in a lawn field to define the working domain is much easier and more flexible than installing the perimeter wire required by existing autonomous lawnmowers. The common approach of existing bearing-only SLAM methods relies on a motion model for predicting the robot’s pose and a sensor model for updating the pose. In the motion model, the error on the estimates of object positions is cumulated due mainly to the wheel slippage. Quantifying accu- rately the uncertainty of object positions is a fundamental requirement. In bearing-only SLAM, the Probability Density Function (PDF) of landmark position should be uniform along the observed bearing. Existing methods that approximate the PDF with a Gaussian estimation do not satisfy this uniformity requirement. This thesis introduces both geometric and proba- bilistic methods to address the above problems. The main novel contribu- tions of this thesis are: 1. A bearing-only SLAM method not requiring odometry. The proposed method relies solely on the sensor model (landmark bearings only) without relying on the motion model (odometry). The uncertainty of the estimated landmark positions depends on the vision error only, instead of the combination of both odometry and vision errors. 2. The transformation of the spatial uncertainty of objects. This thesis introduces a novel method for translating the spatial un- certainty of objects estimated from a moving frame attached to the robot into the global frame attached to the static landmarks in the environment. 3. The characterization of an improved PDF for representing landmark position in bearing-only SLAM. The proposed PDF is expressed in polar coordinates, and the marginal probability on range is constrained to be uniform. Compared to the PDF estimated from a mixture of Gaussians, the PDF developed here has far fewer parameters and can be easily adopted in a probabilistic framework, such as a particle filtering system. The main advantages of our proposed bearing-only SLAM system are its lower production cost and flexibility of use. The proposed system can be adopted in other domestic robots as well, such as vacuum cleaners or robotic toys when terrain is essentially 2D.