Terrain Parameters Research Articles

Decision-Making for Autonomous Mobility Using Remotely Sensed Terrain Parameters in Off-Road Environments

<div class="section abstract"><div class="htmlview paragraph">Off-road vehicle operation requires constant decision-making under great uncertainty. Such decisions are multi-faceted and range from acquisition decisions to operational decisions. A major input to these decisions is terrain information in the form of soil properties. This information needs to be propagated to path planning algorithms that augment them with other inputs such as visual terrain assessment and other sensors. In this sequence of steps, many resources are needed, and it is not often clear how best to utilize them. We present an integrated approach where a mission’s overall performance is measured using a multiattribute utility function. This framework allows us to evaluate the value of acquiring terrain information and then its use in path planning. The computational effort of optimizing the vehicle path is also considered and optimized. We present our approach using the data acquired from the Keweenaw Research Center terrains and present some results.</div></div>

Аналіз стану водотоків суббасейну р.Cян як об’єкт водного кадастру

Among the existing state cadastres, an important place belongs to the water, which is the basis of state management of the water fund. According to the Water Code of Ukraine, the water fund includes all water bodies, provides for the systematic determination of reserves and quality of water resources in a particular area.Water quality is affected by a large number of factors, including location, water body category, water intake area, water mirror area, water volume, river network type, terrain parameters and properties, hydrological conditions. No less important is the influence of anthropogenic factors, which is correlated with the degree of development of the territory, land structure, population and settlements, man-made load and so on. The purpose of the work is to analyze the state of watercourses in the Syan river sub-basin and to establish the main factors that affect water quality. The quality of water resources has a great impact on public health, so a careful analysis of the state of natural water sources is one of the important components of the cadastre of water resources. Method. During the research we worked with information provided by the Lviv Hydrometeorological Service, Lviv egional Department of Water Management, research laboratories engaged in monitoring and analysis of water resources of Lviv region for the last ten years, data from the environmental passport of Lviv region for 2020. During processing of results used a combination of methods of system and structural analysis, analytical comparison, mathematical formalization, cartographic materials, which provided the ability to solve problems and achieve the goal and the reliability of conclusions [Sovhira S. V. et al., 2016]. Results. It is established that the water quality in watercourses flowing through the territory that does not carry a man-made load depends mainly on natural factors, namely: territorial location, climatic conditions, relief, hydrogeological and geomorphological conditions, etc. Due to climate change, various natural phenomena and problems with flooding are increasingly occurring, which negatively affect water resources and quality. Floods occur for various reasons and, in recent years, are characteristic of the Syan river sub-basin, especially in mountainous areas. For the territory characterized by high economic development, the water quality of rivers depends: on mining activities; soil pollution; creation and operation of artificial reservoirs; economic activity within the floodplainchannel complexes; dredging and channel-directing works; deforestation and intensive economic activity at watersheds, which is clearly confirmed by the state of water in the Shklo river and its tributaries Gnoyanets, Tereshka, etc. Data on the state of watercourses in the Syan river sub-basin were collected, systematized and analyzed using and adhering to the main provisions of the integrated approach to water resources management according to the basin principle. Scientific novelty and practical significance. It is proposed to use an integrated approach to create water resources management plans based on the basin principle on the example of the Syan river sub-basin. This approach will allow to identify all sources of influence, their nature, relationship and interaction and to fill with official, relevant data geoportal “State Water Cadastre” on the website of the State Water Agency.

Assessing the effect of lithological setting, block characteristics and slope topography on the runout length of rockfalls in the Alps and on the island of La Réunion

Abstract. In four study areas within different lithological settings and rockfall activity, lidar data were applied for a morphometric analysis of block sizes, block shapes and talus cone characteristics. This information was used to investigate the dependencies between block size, block shape and lithology on the one hand and runout distances on the other hand. In our study, we were able to show that lithology seems to have an influence on block size and shape and that gravitational sorting did not occur on all of the studied debris cones but that other parameters apparently control the runout length of boulders. Such a parameter seems to be the block shape, as it plays the role of a moderating parameter in two of the four study sites, while we could not confirm this for our other study sites. We also investigated the influence of terrain parameters such as slope inclination, profile curvature and roughness. The derived roughness values show a clear difference between the four study sites and seem to be a good proxy for block size distribution on the talus cones and thus could be used in further studies to analyse a larger sample of block size distribution on talus cones with different lithologies.

Impact of global warming on snow in ski areas: A case study using a regional climate simulation over the interior western United States

AbstractA high-resolution (4 km) regional climate simulation conducted with the Weather Research and Forecast (WRF) model is used to investigate potential impacts of global warming on skiing conditions in the interior western United States (IWUS). Recent past and near-future climate conditions are compared. The past climate period is from November 1981 to October 2011. The future climate applies to a 30-year period centered on 2050. A pseudo global warming approach is used, with the driver re-analysis dataset perturbed by the CMIP5 ensemble mean model guidance. Using the 30-year retrospective simulation, a vertical adjustment technique is used to determine meteorological parameters in the complex terrain where ski areas are located. For snow water equivalent (SWE), Snow Telemetry sites close to ski areas are used to validate the technique and apply a correction to SWE in ski areas. The vulnerability to climate change is assessed for 71 ski areas in the IWUS considering SWE, artificially produced snow, temperature, and rain. 20 of the ski areas will tend to have fewer than 100 days per season with sufficient natural and artificial snow for skiing. These ski areas are located at either low elevations or low latitudes making these areas the most vulnerable to climate change. Throughout the snow season, natural SWE decreases significantly at the low elevations and low latitudes. At higher elevations changes in SWE are predicted to not be significant in the mid-season. In mid-February, SWE decreases by 11.8% at the top elevations of ski areas while it decreases by 25.8% at the base elevations.

Representativeness of FLUXNET Sites Across Latin America

AbstractEnvironmental observatory networks (EONs) provide information to understand and forecast the spatial and temporal dynamics of Earth's biophysical processes. Consequently, representativeness analyses are important to provide insights for improving EONs' management, design, and interpretation of their value‐added products. We assessed the representativeness of registered FLUXNET sites (n = 41, revised on September 2018) across Latin America (LA), a region of great importance for the global carbon and water cycles, which represents 13% of the world's land surface. Nearly 46% of registered FLUXNET sites are located in evergreen broad‐leaf forests followed by sites in woody savannas (∼20%). Representativeness analyses were performed using a 0.05° spatial grid for multiple environmental variables, gross primary productivity (GPP), and evapotranspiration (ET). Our results showed a potential representativeness of 34% of the surface area for climate properties, 36% for terrain parameters, 34% for soil resources, and 45% when all aforementioned environmental variables were summarized into a principal component analysis. Furthermore, there was a 48% potential representativeness for GPP and 34% for ET. Unfortunately, data from these 41 sites are not all readily available for the scientific community, limiting synthesis studies and model benchmarking/parametrization. The implication is that global/regional data‐driven products are forced to use information from FLUXNET sites outside LA to predict patterns in LA. Representativeness could increase to 86% (for GPP) and 80% (for ET) if 200 sites are optimally distributed. We discussed ongoing challenges, the need to enhance interoperability and data sharing, and promote monitoring efforts across LA to increase the accuracy of regional‐to‐global data‐driven products.

Three-Dimensional Mapping of Forest Soil Carbon Stocks Using SCORPAN Modelling and Relative Depth Gradients in the North-Eastern Lowlands of Germany

To cope with the challenges in forest management that are contemporarily caused by climate change, data on current chemical and physical soil properties are more and more necessary. For this purpose, we present a further amalgam of depth functions and SCORPAN modelling to provide data at arbitrary depth layers. In this concept, regionalisation is split up into the modelling of plot totals and the estimation of vertical distributions. The intended benefits by splitting up are: consistency between estimates on plot level and depth layer level, avoidance of artificial depth gradients, straightforward interpretation of covariates in the sense of pedogenetic processes, and circumnavigation of the propagation of uncertainties associated with separation between horizons during field sampling. The methodology was tailored to the circumstances within the north-eastern lowlands and the utilisation of current inventory data of the National Forest Soil Inventory (NFSI) in Brandenburg (Germany). Using the regionalisation of soil organic carbon (SOC) as an example, the application is demonstrated and discussed in detail. The depth to groundwater table and terrain parameters related to the catchment area were the main factors in SOC storage. The use of kriging did not improve the model performance. The relative depth gradients of SOC were especially distinguished by tree species composition and stand age. We suppose that interesting fields of application may be found in scenario-based modelling of SOC and when SOC serves as a basis for hydrological modelling.

Stochastic method for prediction of subsidence due to the underground coal mining integrated with GIS, a case study in Serbia

The problem of subsidence and damages on buildings above mining works is an inevitable consequence of underground coal mining in large mining basins. Significant development of various methods for prediction of the displacement parameters of the undermined terrain took place in the period from the middle of the last to the beginning of this century. In this paper, a new mathematical model of the stochastic Pataric-Stojanovic method for the predictive subsidence calculation is developed. A case study, as an example for the practical application of the proposed stochastic method, is given for the underground coal mine “Rembas”- Resavica, Serbia. An original MITSOUKO program package, supported by spatial analyses in the Geographic Information System (GIS), was developed for the predictive subsidence calculation based on the stochastic method proposed by the authors Pataric and Stojanovic. The comparative analysis of predicted and measured subsidence values confirms the possibility of using this stochastic method for predictive subsidence calculation in practice, which is a necessity of modern mining.

Quantifying Topographic Ruggedness Using Principal Component Analysis

The development of geospatial technologies has opened a new era in terms of data collection techniques and analysis procedures. Digital elevation models as 3D visualization of the Earth’s surface have many mapping and spatial analysis applications. The primary terrain factors derived from the raster dataset are usually less critical than secondary ones, e.g., ruggedness index, which plays a vital role in engineering, hydrological information derivation, and geomorphological processes. Surface ruggedness is a significant predictor of topographic heterogeneity by calculating the absolute value of elevation differences within a specified neighborhood surrounding a central pixel. The current study investigates the impacts of various topographic metrics obtained from a digital elevation model on characterizing terrain ruggedness utilizing stepwise principal component analysis. This popular multivariate statistical technique is applied to conduct a comprehensive assessment and treat the information redundancy of terrain parameters. Simultaneously, the standard deviation of elevation is also proposed as an alternative approach to quantifying topographic ruggedness. Besides, quantitative and qualitative method is espoused to validate the algorithms and compare their capabilities to the previously introduced models in the literature. The findings have shown that principal component analysis provides superior performance against other models. Furthermore, they indicated that the standard deviation of elevation could be used instead of the available ones.

An Improved Real-Time Slip Model Identification Method for Autonomous Tracked Vehicles Using Forward Trajectory Prediction Compensation

The complex slip characteristics between the tracks and the terrain make it difficult to build an accurate model for intelligent tracked vehicles. Both the path planning and lateral control, however, are highly dependent on the accurate tracked vehicle model. To overcome the issue, a slip model based on the instantaneous centers of rotation (ICRs) of tracks and a dual-layer adaptive unscented Kalman filter (DAUKF) are used to estimate the ICR locations in real-time without requiring prior knowledge of terrain parameters. First, the historical trajectory information is used to estimate ICR locations by the upper-layer AUKF estimator preliminarily. The ICR locations estimated in the upper-layer and the current vehicle state are then imported into the model to predict the future trajectory which can be used to estimate the offset as compensation of preliminarily estimated ICR locations by the lower-layer AUKF estimator. The proposed DAUKF is verified by simulations on MATLAB/Simulink. In order to further verify the effectiveness and practicability of the algorithm, a large number of experiments under different terrains and road conditions are implemented on the electric-drive tracked vehicle. The simulation and experimental results illustrate that the proposed DAUKF can estimate the ICR locations efficiently and accurately, which can improve the accuracy of the tracked vehicle model compared with those of extended Kalman filter (EKF), UKF, and AUKF.

Analysis of shielding failure flash-over rate of collection lines of wind farms in mountainous areas

Aiming at the current situation of serious economic losses caused by lightning shielding on the collection lines of wind farms in mountainous areas. First, this paper uses the horizontal section location map of the collection line to extract the terrain parameters, Then use the catenary equation to determine the spatial position of the conductor and ground of any section of the collector line along the pitch direction, and obtain the height of the conductor and the ground to the ground. Finally, complete the modeling calculation analysis of the three-dimensional electrical geometry model (EGM) of the entire collection line. The calculation results show that, when the thunderstorm day, the arrangement of the collection line and the structure of the tower are the same, the SFFOR of the collection line of the wind farm in mountainous terrain is 0.487 times/100 km/year, and the plain terrain is 0.167 times/100 km/year, that is, mountainous area is 2.9 times of plain terrain; It is recommended to use double lightning protection poles with small protection angles on the collection lines of wind farms in mountainous areas, or install lightning arresters for poles that are prone to flashover to meet the design requirements of line lightning protection.

ANALYSIS OF PARAMETERS OF ROAD TRACKING LIMITATIONS IN TERRITORIAL DESIGN

Due to the need for mass construction of transport routes there is a need to develop theoretical foundations and methods of their design, which will simplify the design process, develop geometric models to optimize transport links, improve existing and create new methods of geometric modeling of optimal trajectories. terrain and road parameters. In the process of designing transport links, the main objective function is the economic effect of their construction. The cost-effectiveness of the overpass should include minimizing the length of the road, taking into account construction costs, which also depend on the minimum allowable parameters of transport routes, the peculiarities of traffic. To develop an optimization model for determining the trajectory of the road in the territorial design, the analysis of geometric parameters of the features of transport routes such as the minimum: turning radius in terms of Rgor; profile angle of rise of the path angle α; profile angle of descent path angle β, profile radius of path convex curves Rprof1; profile radius of the path concave curves Rprof2; step of supporting structures of engineering supports ∆L. And also the maximum height of transport engineering support ∆H. These indicators will serve as limitations in optimizing the trajectory of overpasses. А conditional classification of relief surfaces taking into account geometrical features and parameters of different types of overpasses is offered. Its use will promote increase of quality of designing of transport ways on a terrain relief, simplification of design process and improvement of quality of design planning decisions at territorial designing. The further use of the optimization model to determine the trajectory of roads for different types of transport will improve the quality of design, solving planning problems of spatial design.

Predictive mapping of soil electrical conductivity as a Proxy of soil salinity in south-east of Algeria

In semi-arid and arid areas soil salinity has adverse effects both on the environment and agricultural production. The region of Biskra (South-East of Algeria) underwent a strong agricultural transformation from traditional oasis agriculture to an almost exclusive production of dates involving market gardening throughout the year. The main goal was to predict the spatial variation of EC using geostatistics and a Geographic Information System (GIS), comparing also the performance of two classical geostatistical interpolators - Ordinary Kriging (OK), using only point data, and Cokriging (CK), introducing also auxiliary variables to improve prediction accuracy (SI gypsum and SO 4 2− , obtained from the analysis of the chemical and geochemical processes of soil salinization). For this study, a total of 42 soil samples were randomly collected from topsoil (0–15 ​cm) in the irrigated perimeter of El Ghrous, a representative rural community located in the west of Biskra. Aiming to better understand the processes that most influence the evolution of soil salinity in this area, some chemical parameters were determined, among which the electrical conductivity (EC). Moreover, some terrain parameters were derived from a digital elevation model as auxiliary information, and Normalized Difference Vegetation Index (NDVI) was calculated from satellite imagery. The prediction efficiency of the methods was evaluated by calculating the mean error (ME) and the root mean square error (RMSE). The resulting maps showed that soils in the study area are affected by salinization. Cross-validation results showed a better performance in estimating EC of CK, after the introduction of the covariates, than OK, with an RMSE value of 0.92 vs. 1.53. This suggests a greater efficiency of CK in EC prediction in this area, confirming that the introduction of some auxiliary data correlated to the target variable significantly improves the interpolation. A third kriging technique, Indicator Kriging (IK) was applied to generate a map of the probability of exceeding a given threshold. • Comparison of two different EC mapping techniques. • Introduction of auxiliary variables related to EC to improve the interpolation results. • Use of IK to individuate areas with a high probability of exceeding a salinity threshold. • Production of estimated maps for regional planning and for future agricultural projects.

An Integrated Terrain Identification Framework for Mobile Robots: System Development, Analysis, and Verification

Terrain identification is essential to autonomous control algorithm development for mobile robots. This article proposes an integrated framework to identify terrain parameters based on inertial, and driving current signals. Multiple sources are combined to reduce the instability caused by single signals. A dynamic model of the track-soil system is established as the theoretical basis of identification. All signals are processed in the time, frequency, and time-frequency domains. The features of each domain are generated by statistical methods. To analyze, and select superior feature categories, a maximum-relevance, and minimum-redundancy criterion based on Pearson's correlation is proposed to evaluate the priority of features. A probabilistic neural network is used to identify the category of terrain. All results are analyzed with two factors, source, and input, to find the most effective rule of the proposed framework. The crossing combination analysis is taken into consideration to explore all potential improvement. The results show that the driving current yields comparative identification accuracy as inertial signals. Compared to the single signal source, the method using the combined signal source can effectively improve the accuracy of terrain identification.

Resolving Biases in DEM Differencing for Estimation of Change in Elevation of Glacier Surfaces Using Cartosat-I Stereo Data

Understanding the impact of climatic variations on Himalayan-Karakoram glaciers has now become a necessity for planning water resources of Indus-Ganga–Brahmaputra basins. One of the most important parameters to assess this is the mass balance of glaciers. Besides ground-based studies of mass balance, differencing of Digital Elevation Models (DEMs) is now commonly employed for estimating the long-term changes for this purpose. Before applying this approach, it is necessary to evaluate the accuracy of elevation changes of glacier surfaces with respect to terrain parameters and the DEMs used. This study demonstrates the factors governing DEM differencing using an example of four glaciers of Zanskar region by comparing DEMs extracted from Cartosat-1 stereo data of ablation season of the years 2005 and 2015. A strong relation has been observed between slope of the terrain and elevation changes between the two DEMs on non-glaciated stable terrain (R2 = 0.8144). The regression equation thus developed has been used to correct elevation changes on glacier surface. Lowering of ablation zone of the four sample glaciers over a period of ten years has been of the order of 3.92, 11.17, 4.99 and 4.39 m. The results obtained by using Cartosat-I DEMs have also been validated using ASTER DEMs of similar time interval.

Формирование цифровой модели влажности почв на основе показателей рельефа местности и по материалам лесоустройства

Формирование цифровой модели влажности почв на основе показателей рельефа местности и по материалам лесоустройства

Classification of Landforms for Digital Soil Mapping in Urban Areas Using LiDAR Data Derived Terrain Attributes: A Case Study from Berlin, Germany

In this study, a knowledge-based fuzzy classification method was used to classify possible soil-landforms in urban areas based on analysis of morphometric parameters (terrain attributes) derived from digital elevation models (DEMs). A case study in the city area of Berlin was used to compare two different resolution DEMs in terms of their potential to find a specific relationship between landforms, soil types and the suitability of these DEMs for soil mapping. Almost all the topographic parameters were obtained from high-resolution light detection and ranging (LiDAR)-DEM (1 m) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-DEM (30 m), which were used as thresholds for the classification of landforms in the selected study area with a total area of about 39.40 km2. The accuracy of both classifications was evaluated by comparing ground point samples as ground truth data with the classification results. The LiDAR-DEM based classification has shown promising results for classification of landforms into geomorphological (sub)categories in urban areas. This is indicated by an acceptable overall accuracy of 93%. While the classification based on ASTER-DEM showed an accuracy of 70%. The coarser ASTER-DEM based classification requires additional and more detailed information directly related to soil-forming factors to extract geomorphological parameters. The importance of using LiDAR-DEM classification was particularly evident when classifying landforms that have narrow spatial extent such as embankments and channel banks or when determining the general accuracy of landform boundaries such as crests and flat lands. However, this LiDAR-DEM classification has shown that there are categories of landforms that received a large proportion of the misclassifications such as terraced land and steep embankments in other parts of the study area due to the increased distance from the major rivers and the complex nature of these landforms. In contrast, the results of the ASTER-DEM based classification have shown that the ASTER-DEM cannot deal with small-scale spatial variation of soil and landforms due to the increasing human impacts on landscapes in urban areas. The application of the approach used to extract terrain parameters from the LiDAR-DEM and their use in classification of landforms has shown that it can support soil surveys that require a lot of time and resources for traditional soil mapping.

DETERMINATION OF OPTIMAL LOCATION FOR SETTING UP CELL PHONE TOWER IN CITY ENVIRONMENT USING LIDAR DATA

Abstract. Cell phones have become an inherent part of human life and have grown rapidly in the last decade. In India, there are nearly 120 crore cell phone users which require setting up of cell phone tower at an appropriate location to transmit the signals. A signal strength that is measured in (dBm) keeps on varying from one location to another. Over the decades, there has been a great deal of concern about placing a cell phone tower to manage adequate signal strength for an area. During transmission, the signals get affected by the position of building, ground and the distances the signals need to travel before reaching any receiver or user location. Existing researches focus on the requirement of a suitable number of cell phone towers for a big area in a GIS environment. Depending on the building and other infrastructure present in an area an optimal location can be determined for setting up the cell phone tower. However, the detailed 3D data is required for it. In this paper, a LiDAR-based technique is attempted. Using the point cloud data of the RGIPT campus, features like building, ground, obstruction points, etc are extracted. To determine the transmission paths for the signal, building/object boundary(es), etc. coming in the path(s) between the cell phone tower and the receiver location are determined. Once the detailed paths for the signal transmission i.e, direct path, or path after diffraction (around the buildings), and/or reflection (from the wall and ground) are determined, terrain parameters (distance, path difference, attenuation, etc) are ascertained. These are then used to model and determine the relative signal strength for any receiver location. The position of cell phone tower is then tested for optimal XY, and Z position to ascertain the best location for setting up the cell phone tower. The method is verified against various path determination algorithms. A centroid and viewshed based approach is adopted here. The technique is generic, novel and essentially work with LiDAR point data without needing DEM and can be applied for any terrain condition.

COLLABORATIVE NOISE MAPPING USING SMARTPHONE

Abstract. Noise pollution is considered to be one of the most prevalent environmental challenges affecting human health. Noise pollution is increasing in cities needing techniques to monitor and predict the noise. The monitoring of traffic noise levels in different parts of the cities at different times has become very difficult due to logistic constraints. It is thus required to measure the noise levels at certain strategic locations, such as, near the noise sources (e.g., roads), and then to utilize it to predict the noise levels at surrounding locations. The challenge of monitoring the noise near several road crossings in a city can be reduced using a smartphone-based noise monitoring technique. However, the prediction of noise levels and showcase it as maps require terrain data, noise data, and noise prediction models. The requirement of terrain data can be met using open-source terrain data, from which various terrain parameters can be extracted and integrated with a standard prediction model on the web platform to predict the noise map for an area. Smartphone-based noise monitoring and its subsequent mapping can be a very popular and effective option, which uses a crowdsourcing approach. The entire methodology is tried to be applied over Lucknow city in India. Noise levels are monitored at three different slots, daily, over 14 road crossings using the smartphone-based app. Further, collected noise levels were calibrated against standard noise meter to ascertain accurate noise levels for these locations. Thereafter, three categories of noise environments are chosen from it and mapped using open-source satellite images and standard noise models, over the web on the GIS platform. The predicted noise levels on the maps were verified with the recorded noise data from similar locations using standard noise meter. For these three crossings at different times the predictions are found to be accurate within ±4.5 dB.

A characteristic periglacial landform: Automated recognition and delineation of cryoplanation terraces in eastern Beringia

AbstractAutomated recognition and delineation of specific landforms and their constituent elements ranks among the most active areas of contemporary geomorphological research. This study contributes to that literature by applying semi‐ and fully automated recognition procedures to upland periglacial geomorphic landscapes. The Cryoplanation Terrace semi‐Automated Recognition (CTAR) algorithm utilizes basic terrain parameters to identify locations of cryoplanation terraces (CTs) from the high‐resolution ArcticDEM. Using a multistep process, candidate areas are identified based on morphometric characteristics. CTAR uses terrain derivatives to search ridges, hills, and mountains for flat areas bounded by abrupt breaks in slope. Because CTs are found exclusively in upland periglacial environments, some locations require that low‐lying areas be filtered out. To assess accuracy, CTAR was tested at five local study sites distributed across eastern Beringia, each containing multiple CTs delimited manually in a previous study. CTAR performed well, with an overall accuracy of 90%. A strong linear relationship exists between the size of CTAR‐delimited terraces and those identified in a previous study through air‐photo interpretation. In addition to identifying nearly all of the CTs in the five study areas, a fully automated version of the algorithm (GEE‐CTAR), implemented in Google Earth Engine, identified nearly 8,000 previously unmapped potential CTs in the Seward Peninsula region of western Alaska. The ability to identify CTs from digital elevation models provides a useful tool for recognizing and delineating upland periglacial topography. Objective recognition of large erosional landform elements created by periglacial processes is a critical step in developing the field of periglacial geomorphometry.

A UGV-based modular robotic manipulator for soil sampling and terramechanics investigations

Unmanned vehicles are a natural choice for accessing challenging or hazardous terrains, for instance oil sands tailings ponds, and performing tasks such as soil sampling and terramechanics investigations. In previously published work, an unmanned ground vehicle (UGV) named RTC-I was designed and built for this task by part of our group. The present article covers the design choices and technical details of a custom-built robotic manipulator, a soil sampler, and an instrumented wheel deployed onboard a second-generation UGV named RTC-II. The robotic manipulator is designed to provide the reach, payload capacity, ruggedness, and self-locking operation required for field operations. The soil sampler employs a curved scoop to minimize deformation of the collected sample. The instrumented wheel permits independent control of the normal load and the slip ratio during terramechanics investigations. Each of the three designs is deployed and successfully tested in field experiments. Measurements collected by the soil sampler and instrumented wheel will be used in future work dealing with sampler force modeling and real-time terrain parameter estimation, respectively.