Analysis of terrain geometry representations for traversability of a Mars rover

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.

A near-to-far non-parametric learning approach for estimating traversability in deformable terrain

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

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.

Analyzing the impact of learning inputs on near-to-far terrain traversability estimation

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.

Nonparametric traversability estimation in partially occluded and deformable terrain

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.

Analysing the Impact of Learning Inputs - Application to Terrain Traversability Estimation

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.

Digital elevation model error in terrain analysis

Digital elevation models (DEMs) have been an important topic in geography and surveying sciences for decades due to their geomorphological importance as the reference surface for gravita-tion-driven material flow, as well as the wide range of uses and applications. When DEM is used in terrain analysis, for example in automatic drainage basin delineation, errors of the model collect in the analysis results. Investigation of this phenomenon is known as error propagation analysis, which has a direct influence on the decision-making process based on interpretations and applications of terrain analysis. Additionally, it may have an indirect influence on data acquisition and the DEM generation. The focus of the thesis was on the fine toposcale DEMs, which are typically represented in a 5-50m grid and used in the application scale 1:10 000-1:50 000. The thesis presents a three-step framework for investigating error propagation in DEM-based terrain analysis. The framework includes methods for visualising the morphological gross errors of DEMs, exploring the statistical and spatial characteristics of the DEM error, making analytical and simulation-based error propagation analysis and interpreting the error propagation analysis results. The DEM error model was built using geostatistical methods. The results show that appropriate and exhaustive reporting of various aspects of fine toposcale DEM error is a complex task. This is due to the high number of outliers in the error distribution and morphological gross errors, which are detectable with presented visualisation methods. In ad-dition, the use of global characterisation of DEM error is a gross generalisation of reality due to the small extent of the areas in which the decision of stationarity is not violated. This was shown using exhaustive high-quality reference DEM based on airborne laser scanning and local semivariogram analysis. The error propagation analysis revealed that, as expected, an increase in the DEM vertical error will increase the error in surface derivatives. However, contrary to expectations, the spatial au-tocorrelation of the model appears to have varying effects on the error propagation analysis depend-ing on the application. The use of a spatially uncorrelated DEM error model has been considered as a 'worst-case scenario', but this opinion is now challenged because none of the DEM derivatives investigated in the study had maximum variation with spatially uncorrelated random error. Sig-nificant performance improvement was achieved in simulation-based error propagation analysis by applying process convolution in generating realisations of the DEM error model. In addition, typology of uncertainty in drainage basin delineations is presented.

Archaean crustal evolution in eastern Finland : New geochronological and geochemical constraints from the Kuhmo terrain and the Nurmes belt

This thesis summarises the results of four original papers concerning U-Pb geochronology and geochemical evolution of Archaean rocks from the Kuhmo terrain and the Nurmes belt, eastern Finland. The study area belongs to a typical Archaean granite-greenstone terrain, composed of metavolcanic and metasedimentary rocks in generally N-S trending greenstone belts as well as a granitoid-gneiss complex with intervening gneissic and migmatised supracrustal and plutonic rocks. U-Pb data on migmatite mesosomes indicate that the crust surrounding the Tipasjärvi-Kuhmo-Suomussalmi greenstone belt is of varying age. The oldest protolith detected for a migmatite mesosome from the granitoid-gneiss complex is 2.94 Ga, whereas the other dated migmatites protoliths have ages of 2.84 2.79 Ga. The latter protoliths are syngenetic with the majority of volcanic rocks in the adjacent Tipasjärvi-Kuhmo-Suomussalmi greenstone belt. This suggests that the genesis of some of the volcanic rocks within the greenstone belt and surrounding migmatite protoliths could be linked. Metamorphic zircon overgrowths with ages of 2.84 2.81 Ga were also obtained. The non-migmatised plutonic rocks in the Kuhmo terrain and in the Nurmes belt record secular geochemical evolution, typical of Archaean cratons. The studied tonalitic rocks have ages of 2.83 2.75 Ga and they have geochemical characteristics similar to low-Al and high-Al TTD (tonalite-trondhjemite-dacite). The granodiorites, diorites, and gabbros with high Mg/Fe and LILE-enriched characteristics were mostly emplaced between 2.74 2.70 Ga and they exhibit geochemical characteristics typical of Archaean sanukitoid suites. The latest identified plutonic episode took place at 2.70 2.68 Ga, when compositionally heterogeneous leucocratic granitoid rocks, with a variable crustal component, were emplaced. U-Pb data on migmatite leucosomes suggest that leucosome generation may have been coeval with this latest plutonic event. On the basis of available U-Pb and Sm-Nd isotopic data it appears that the plutonic rocks of the Kuhmo terrain and the Nurmes belt do not contain any significant input from Palaeoarchaean sources. A characteristic feature of the Nurmes belt is the presence of migmatised paragneisses, locally preserving primary edimentary structures, with sporadic amphibolite intercalations. U-Pb studies on zircons indicate that the precursors of the Nurmes paragneisses were graywackes that were deposited between 2.71 Ga and 2.69 Ga and that they had a prominent 2.75 2.70 Ga source. Nd isotopic and whole-rock geochemical data for the intercalated amphibolites imply MORB sources. U-Pb data on zircons from the plutonic rocks and paragneisses reveal that metamorphic zircon growth took place at 2.72 2.63 Ga. This was the last tectonothermal event related to cratonisation of the Archaean crust of eastern Finland.

The Contested Terrain of Globalization: Review and Conceptual Clarifications for Management Studies

The concept of globalization has become a shorthand for making sense of contemporary society. It reflects large-scale economic and social change, which affects people differently and evokes different viewpoints. Globalization is thus a highly contested concept and phenomenon. Contradictory and competing views, in turn, seem to be based on different interpretations of the present dominant forms of globalization, and of the material, economic, social and cultural conditions that these forms produce and give rise to. We view globalization not only as a significant set of economic, financial, social, political and cultural forces but as a powerful and contested discursive space. In this article, we present an overview of recent literature to introduce different thematic perspectives on globalization, to specify different ideological and discursive bases to approach globalization, and to place multinational corporations (MNC:s) within this context. Our account is not exhaustive, rather, it is intended as a basis for further discussion on the nature and role of multinational corporations in complex ”global” society

ILS glideslope evaluation for imperfect terrain

Instrument landing systems (ILS) are normally designed assuming the site around them to be flat. Uneven terrain results in undulations in the glidescope. In recent years, models have been evolved for predicting such aberrations as a simpler alternative to experimental methods. Such modeling normally assumes the ground to be fully conducting. A method is presented for considering imperfect terrain conductivity within the framework of the uniform theory of diffraction (UTD). A single impedance wedge formulation is developed to a form that resembles the standard form of UTD, with only one extra term in the diffraction coefficient. This extends the applicability of the standard UTD formulation and software packages to the case of the imperfectly conducting terrain. The method has been applied to a real airport site in India and improved agreement with measured glidescope parameters is demonstrated

Correlation filtering: a terrain correction method for aeromagnetic maps with application

In correlation filtering we attempt to remove that component of the aeromagnetic field which is closely related to the topography. The magnetization vector is assumed to be spatially variable, but it can be successively estimated under the additional assumption that the magnetic component due to topography is uncorrelated with the magnetic signal of deeper origin. The correlation filtering was tested against a synthetic example. The filtered field compares very well with the known signal of deeper origin. We have also applied this method to real data from the south Indian shield. It is demonstrated that the performance of the correlation filtering is superior in situations where the direction of magnetization is variable, for example, where the remnant magnetization is dominant.

Metamorphic-metasomatic fluids and Al, Si order/disorder of K-rich alkali feldspars from southern Indian high grade terrain

The structural state of K-feldspars in the quartzofeldspathic gneisses, charnockites, metapelites and pegmatites from the southern Kamataka, northern Tamil Nadu and southern Kerala high-grade regions of southern India has been characterized using petrographic and powder X-ray diffraction methods. The observed distribution pattern of structural state with a preponderance of disordered K-feldspar polymorphs in granulites compared to the ordered microclines in the amphibolite facies rocks is interpreted to reflect principally the varying H2O contents in the metamorphic-metasomatic fluids across metamorphic grade. The K-feldspars in the pegmatites of granitic derivation and in a pegmatite of inferred metamorphic origin also point to the important role of aqueous fluids in their structural state.

Calculating the Effect of Uneven Terrain on Glide Path Signals

A method for calculating the bends in the glide slope due to uneven terrain is presented. A computer program written for the purpose enables the calculation of the difference in depth of modulation (DDM) at any point, taking into account the effect of uneven terrain. Results are presented for a hypothetical case of a hill in front of the runway. The program enables us to predict the glide slope bends due to irregular terrain, so that proper selection of glide slope antenna location can be made.

A THREE-WHEELED MOBILE ROBOT FOR TRAVERSING UNEVEN TERRAIN WITHOUT SLIP: SIMULATION AND EXPERIMENTS

A wheeled mobile robot (WMR) can move on uneven terrains without slip if the wheels are allowed to tilt laterally. This paper deals with the analysis, design and experimentations with a WMR where the wheels can tilt laterally. The wheels of such a WMR must be equipped with two degrees of freedom suspension mechanism. A prototype three-wheeled mobile robot is fabricated with a two degree-of-freedom suspension mechanism. Simulations show that the three-wheeled mobile robot can traverse uneven terrains with very little slip and experiments with the prototype on a representative uneven terrain confirm that the slip is significantly reduced.

SPATIO-TEMPORAL DYNAMICS ALONG THE TERRAIN GRADIENT OF DIVERSE LANDSCAPE

Land use (LU) land cover (LC) information at a temporal scale illustrates the physical coverage of the Earth's terrestrial surface according to its use and provides the intricate information for effective planning and management activities. LULC changes are stated as local and location specific, collectively they act as drivers of global environmental changes. Understanding and predicting the impact of LULC change processes requires long term historical restorations and projecting into the future of land cover changes at regional to global scales. The present study aims at quantifying spatio temporal landscape dynamics along the gradient of varying terrains presented in the landscape by multi-data approach (MDA). MDA incorporates multi temporal satellite imagery with demographic data and other additional relevant data sets. The gradient covers three different types of topographic features, planes; hilly terrain and coastal region to account the significant role of elevation in land cover change. The seasonality is another aspect to be considered in the vegetation dominated landscapes; variations are accounted using multi seasonal data. Spatial patterns of the various patches are identified and analysed using landscape metrics to understand the forest fragmentation. The prediction of likely changes in 2020 through scenario analysis has been done to account for the changes, considering the present growth rates and due to the proposed developmental projects. This work summarizes recent estimates on changes in cropland, agricultural intensification, deforestation, pasture expansion, and urbanization as the causal factors for LULC change.