Terrain Characterization Research Articles

Application of active MASW study on irregular and undulated sloping terrain: a case study

ABSTRACT Multichannel Analysis of Surface Waves (MASW) is a geophysical technique used for deciphering subsurface profiling by the determination of shear wave velocity. It is increasingly utilized in sloping terrain; however, its accuracy is limited by the presence of undulating or irregular topography. The study analysed the impact of undulating topography on sloping terrain using models of depressed and elevated topography. The surface waves lose more energy as depth, width, and steepness of the undulated topography increased. The study revealed that the elevated topography had a greater influence than depressed topography. The undulating topography in close proximity to the source resulted in a decrease in resolution dispersion, however the terrain located further away did not have a substantial impact. The study aims to identify suitable configurations that must be taken into account while identifying the subsurface characterization of a sloping terrain using the MASW.

Terrain-Vechicle Traction Control Parameters for Tracked-Robot

Abstract: Here we have the systems of :- 1) Advancements in Vehicle Control :- This summary highlights two recent advancements in vehicle control technology which are the enhanced Handling for Passenger Cars: Researchers have developed a novel control system inspired by expert driver techniques. This system optimizes a car's handling during maneuvers requiring simultaneous acceleration and sharp turns. Simulation testing demonstrates significant performance improvements. Improved Unmanned Ground Vehicle (UGV) Operation on Unstructured Terrain: A new approach addresses the challenges of controlling UGVs on rough terrain. The solution combines sensor fusion for terrain characterization, a neural network for terrain classification, and an adaptive control system for real-time stability adjustments. This multi-pronged approach has been validated through successful experimentation on a UGV platform. 2) Enhancing UGV Control with Haptic Feedback :- A separate study explores the use of haptic feedback to improve UGV control in difficult environments. Traditional operator interfaces often lack sufficient sensory information, leading to reduced situational awareness and operational difficulties. This research proposes a haptic tablet interface that provides the operator with a tactile sense of the UGV's traction state. User studies confirm that this approach significantly reduces the time a UGV spends immobilized due to traction loss. These advancements collectively represent significant progress in vehicle control technology, with potential benefits for both manned and unmanned vehicles operating in challenging scenarios

Are the Noah and Noah-MP land surface models accurate for frozen soil conditions?

The accurate prediction of frozen soil conditions is a critical component of dynamic terrain characterization for engineering operations in cold regions. Currently the trusted resource for dynamic terrain within the US military uses gridded global soil conditions (i.e., temperature and moisture) generated by the Land Information System (LIS) architecture. LIS combines static terrain data with trusted surface weather information into the Noah land surface model to generate a continuous representation of the global surface conditions. Both the input weather data and the model output land surface state are adjusted to match the observed state using an advanced ensemble Kalman filter data assimilation technique to incorporate in situ and remote sensing observations. While this product has been robustly evaluated under unfrozen conditions only sporadic studies have investigated its ability to accurately simulate freeze/thaw cycles, and soil temperature and moisture under frozen conditions. Here, we present a broad comparison and evaluation of the simulated soil state against an in situ network of soil temperature and moisture distributed across the Continental United States that focuses on the winter season. This comparison includes both the Noah and the newer Noah with multi-parameterization (Noah-MP) land surface models. A comparison of the two models reveals substantial differences in simulated soil moisture, frozen content, and frozen soil strength. In general, Noah predicts colder soils with deeper frost depths, and frozen soils that are slightly stronger than Noah-MP. Initial results evaluating Noah and Noah-MP against in situ observations show that both models have significantly degraded model performance for frozen soil conditions compared to their performance for unfrozen soil. In particular, Noah has a significant and persistent cold bias of approximately −3 K when the soil temperature is below freezing. This cold bias is not present in Noah-MP and further analysis shows that this improvement was almost entirely caused by a better representation of thermal conduction through snow in Noah-MP. Specifically, we find that the bulk effective thermal conductivity of a snowpack is 2× greater in Noah, which allows for greater surface heat loss during the winter and, consequently, lower soil temperatures. This analysis reveals that the more advanced multi-layer snow model in Noah-MP substantially improves the representation of soil temperature during the cold season.

Analysis of Topographic Feature Parameters of Dinosaur Valley Ring Tectonic Geomorphology Based on the Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM)

Geomorphological surveys and terrain analysis are essential for geomorphology, hydrology, and geographic information systems (GIS). Terrain characterization parameters are fundamental for comprehending geomorphological processes, delineating landforms, and evaluating geohazards. Hence, this study relies on data from the Gaofen-2 satellite (GF-2) and the Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM). Focusing on the ring tectonics landform of Dinosaur Valley, the research utilizes visual interpretation, GIS terrain parameter extraction techniques, and visual mapping to identify, measure, and analyze terrain features. The results indicate that ALOS DEM can offer accurate geomorphic feature information. The optimal threshold for extracting the surface water system network is 150, and the optimal analysis window for extracting terrain feature parameters is 16 × 16 (0.04 km2), determined through statistical methods. This study reveals distinct double-ring linear features and prominent ring-shaped terrain characteristics by extracting terrain feature parameters and utilizing mathematical and statistical analysis. The topography gradually ascends from the center to the ridgeline and then descends to the valley line, resembling a bowl shape. This study highlights the potential application of ALOS DEM for accurately identifying topographic features in the intricate mountains of the Yunnan Plateau. It establishes a practical research framework for high-precision topographic datasets and geomorphological characterization, facilitating further analysis of the evolution of topographic development and the assessment of tectonic activity.

Deep Learning Vision System for Quadruped Robot Gait Pattern Regulation.

Robots with bio-inspired locomotion systems, such as quadruped robots, have recently attracted significant scientific interest, especially those designed to tackle missions in unstructured terrains, such as search-and-rescue robotics. On the other hand, artificial intelligence systems have allowed for the improvement and adaptation of the locomotion capabilities of these robots based on specific terrains, imitating the natural behavior of quadruped animals. The main contribution of this work is a method to adjust adaptive gait patterns to overcome unstructured terrains using the ARTU-R (A1 Rescue Task UPM Robot) quadruped robot based on a central pattern generator (CPG), and the automatic identification of terrain and characterization of its obstacles (number, size, position and superability analysis) through convolutional neural networks for pattern regulation. To develop this method, a study of dog gait patterns was carried out, with validation and adjustment through simulation on the robot model in ROS-Gazebo and subsequent transfer to the real robot. Outdoor tests were carried out to evaluate and validate the efficiency of the proposed method in terms of its percentage of success in overcoming stretches of unstructured terrains, as well as the kinematic and dynamic variables of the robot. The main results show that the proposed method has an efficiency of over 93% for terrain characterization (identification of terrain, segmentation and obstacle characterization) and over 91% success in overcoming unstructured terrains. This work was also compared against main developments in state-of-the-art and benchmark models.

Geodiversity estimate of the Arxan–Chaihe Volcanic Field extending across two geoparks in Inner Mongolia, NE China

Abstract The Arxan–Chaihe Volcanic Field is located SW of the Great Xing'an Range in Inner Mongolia, NE China. This monogenetic volcanic field formed in the Pleistocene, with its latest activity a fissure eruption c. 2000 years ago. Here we present the first geodiversity estimate for the region, applying a method combining the geomorphological and geological elements into a grading system, weighting their rarity, significance and uniqueness. To outline the geomorphological diversity, the geomorphon concept was used alongside a watershed analysis of the theoretical fluvial network of the region. In addition volcanic geomorphological elements were provided. Available geological maps, field surveys and volcano geology classification were utilized for the geological diversity estimates. Geology and geomorphology are the core parameters of the qualitative–quantitative assessment of geodiversity. Geographical Information Systems (GIS) were used for calculations of geomorphological and geological parameters to provide geodiversity estimates for the Arxan–Chaihe Volcanic Field region. The variables used to estimate the geodiversity include geology (rock rareness from a global perspective) and geomorphology (slope variations). In addition we used channel systems and terrain characterization (geomorphon) to describe the morphodiversity of the study area. Our study assessed recent volcanic features and concluded that they have a high geodiversity and elevated geoheritage value. However, the uplifted and structurally complicated old terrains with mature fluvial networks provide high geomorphological diversity to the region, therefore keeping the overall geodiversity score high.

Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning

In a smart grid communication network, positioning key devices (routers and gateways) is an NP-Hard problem as the number of candidate topologies grows exponentially according to the number of poles and smart meters. The different terrain profiles impose distinct communication losses between a smart meter and a key device position. Additionally, the communication topology must consider the position of previously installed distribution automation devices (DAs) to support the power grid remote operation. We introduce the heuristic method AIDA (AI-driven AMI network planning with DA-based information and a link-specific propagation model) to evaluate the connectivity condition between the meters and key devices. It also uses the link-received power calculated for the edges of a Minimum Spanning Tree to propose a simplified multihop analysis. The AIDA method proposes a balance between complexity and efficiency, eliminating the need for empirical terrain characterization. Using a spanning tree to characterize the connectivity topology between meters and routers, we suggest a heuristic approach capable of alleviating complexity and facilitating scalability. In our research, the interest is in proposing a method for positioning communication devices that presents a good trade-off between network coverage and the number of communication devices. The existing literature explores the theme by presenting different techniques for ideal device placement. Still rare are the references that meticulously explore real large-scale scenarios or the communication feasibility between meters and key devices, considering the detailed topography between the devices. The main contributions of this work include: (1) The presentation of an efficient AMI planning method with a large-scale focus; (2) The use of a propagation model that does not depend on an empirical terrain classification; and (3) The use of a heuristic approach based on a spanning tree, capable of evaluating a smaller number of connections and, even so, proposing a topology that uses fewer router and gateway positions compared to an approach that makes general terrain classification. Experiments in four real large-scale scenarios, totaling over 230,000 smart meters, demonstrate that AIDA can efficiently provide high-quality connectivity demanding a reduced number of devices. Additional experiments comparing AIDA's detailed terrain-based propagation model to the Erceg-SUI Path Loss model suggest that AIDA can reach the smart meter's coverage with a fewer router positions.

Terrain Classification Based on Sensed Leg Compliance for Amphibious Crab Robot

Characterization of terrain in real time allows autonomous legged robots to modify their gait to better suit their environment and recognize hazardous conditions. We present a novel approach for terrain classification for legged robots with passively compliant components, in which terrain information is gathered by measuring component deflection during operation by means of low cost Hall effect magnetometers and embedded magnets. The effectiveness of this approach is demonstrated on a hexapod robot designed to operate in the surf zones of beaches. Datasets of sensor measurements corresponding to three types of granular terrain are collected in both a laboratory and outdoor beach environment, and are used to train support vector machine (SVM) classifiers. Results show that, using time domain features of measurements from each leg, a mean accuracy of 99.3% and 92.4% through 10-fold cross-validation can be achieved for the laboratory and beach datasets, respectively. For the beach dataset, this accuracy is found to be slightly greater than for a classifier trained on measurements from an on-board inertial measurement unit (IMU) (91.6%), while an accuracy of 95.1% can be achieved by combining the information from both sensor modalities as inputs to a single classifier. The effect of the Earth’s magnetic field on the measurements and considerations for data collection are also discussed.

Digital Terrain Characterization of Nilona Micro Watershed of Darwha Block of Maharastra

Watershed characterization is the first step in the sustainable management of watershed resources. Morphometric analysis of a watershed using Digital Elevation Model (DEM) provides a quantitative description of the drainage system which is an important aspect of the characterization of watersheds. The study was conducted in Nilona micro-watershed covering an area of 1297.35 ha in Darwha tehsil of Yavatmal district, Maharashtra. The terrain attributes and drainage configuration were derived from the Cartosat-1 data, 10m resolution Digital Terrain Model (DTM) using ArcGIS. Surface soil samples of 118 locations were collected from grid points located at regular interval of 325 m. The digital terrain analysis showed that slope varies from 0 to 45.9 percent, with a mean value of 4.5%. Most of the area of Nilona micro-watershed was classified as gentle sloping. Profile curvature varies from -5.1 to 4.6 m m-1, respectively indicating the coexistence of erosive as well as depositional landforms. Overall, the study documents the utility of site-specific modeling and geo-statistical interpolation based predictive mapping for watershed planning.

Mapping of major river terraces and assessment of their characteristics in Upper Pindar River Basin, Uttarakhand: A geospatial approach

In the present paper, an attempt has been made to trace out the major terraces and analyse their characteristics in the Upper Pindar valley of the Uttarakhand state. The study is based on digital elevation model (DEM), satellite data-based terrain characterisation with field and ground truth investigations. The results are derived from the data, which shows the preservation of river terraces along the trunk and tributary streams of the Pindar River. The terraces are formed because of the combined effect of local structural, geological, lithological, and tectonic control along with the climatic influence on landform development. Among the eight surveyed terraces, seven are cut and fill type and influenced by climate, tributaries, formed by both glacial and fluvio-glacial processes, whereas the rest terrace is developed because of tectonic uplift and creates a strath type. The terrace of Upthar and T3 of Langari has gone through the past tectonic events operated within this region. The analysis through cross-section profiles also exhibits the fact that the Upper Pindar basin has experienced a maximum of three stages of terrace development during different phases of the Holocene period and Late Pleistocene period. The alternative river aggradation, degradation, local landsliding, and a sudden increase in sediment flux caused by the monsoonal surge, tectonic uplift, and rapid incision in three separate times are the prime factors that are responsible for the generation of maximum three-tiered terrace landform in the study area. The sequence of terrace development reveals that the trunk of the Pindar River creates the highest number of terraces followed by the tributary rivers, which are generated in the later phase. The three phases of river aggradation in 33.5 ka, 17.5–13 ka, and 8–3.6 ka separated by a deep incision of different timescale are remarkable in this area, mainly around Langri and Khati, which creates the ordering of a maximum of three levels of terrace development. The results concluded that the dominance of local processes plays a vital role in the formation and characterization of river terraces in the Upper Pindar valley. The present study gives a detailed idea of the valley development and the result can also be used as an identification of suitable areas for human interactions with the environment.

Evaluation of geomorphometric characteristics and soil properties after a wildfire using Sentinel-2 MSI imagery for future fire-safe forest

Understanding spatiotemporal geomorphological and pedological changes as a consequence of wildfires can allow stakeholders, land planners, and policymakers to design efficient fire safety-based afforestation and restoration programs of forest lands. The use of remote sensing techniques is a key tool to achieve this goal. The suitable combination of Sentinel-2 MSI data for mapping of different spectral indices related to burn severity and their relationship with other morphometric and soil properties can contribute to a better understanding of the impact of fire, and this is relevant in regions where is still scarce fire-related research such as Turkey. In this investigation, the use of NDVI (Normalized Difference Vegetation Index), dNDVI (Difference Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), NBR (Normalized Burn Ratio), dNBR (Difference Normalized Burn Ratio), RBR (Relativized Burn Ratio), SBI (Soil Bare Index), As (Upslope area), CTI (Compound Topographic Index), TCI (Terrain Characterization Index), SPI (Stream Power Index) and Curvature (Standard Curvature) were combined. As a study case, 47.43 ha in a burned area of Çınarpınar forest unit, Andırın, Kahramanmaraş in Turkey was selected. The results showed that dNDVI, dNBR, RBR, SBI contribute to relevant information about the effect of the wildfire. According to the dNBR fire severity classification, 75% of the total area has been exposed to high-severity fire. The relationship of Sentinel MSI satellite images with some soil and morphometric features have been found meaningful to understand the impact of forest fire in Mediterranean ecosystems. The information collected in the Turkish forest areas affected by wildfires should be relevant for planning and represent a key contribution to the selection of restoration programs and afforestation techniques for a future fire-safe forest.

New type of sand wedge polygons in the salt cemented soils of the hyper-arid Atacama Desert

Polygonal grounds are landscape features commonly associated with periglacial environments originating from freeze-thawing cycles or frost-related processes. However, such a genesis is unlikely for polygonal grounds on alluvial surfaces in the warm and hyper-arid Atacama Desert due to the lack of enduring sub-zero temperatures and limited water availability, whereas a cracking mechanism based on thermal contraction and/or desiccation is more plausible. To differentiate between those mechanisms, we performed a quantitative morphometric terrain characterization in combination with a geochemical and sedimentological analysis on three polygonal networks located in the Yungay area of the Atacama Desert, Chile. Our data show that these sand wedge polygons differ from other polygonal features in the Yungay area such as salt polygons and mud crack polygons from playa environments in regard to composition, morphometry and topographical setting. The investigated polygonal soils are composed of siliciclastic sediment that is mainly cemented by sulfates (gypsum & anhydrite) in the shallow ground (~0–50 cm) and by nitrates and chlorides in the deeper ground (~50–100 cm) while being separated by about 1 m deep, salt-poor and V-shaped sand wedges. The low clay content (~2 wt%) makes an exclusive desiccation origin less relevant whereas the high salt content (63 wt%) and the high surface temperature variations make thermal contraction origin more likely. Morphometric data indicate a link between topography and polygon geometry, as the flat-centered polygons (mean size 3.9 to 4.7 m) are aligned either in slope direction or perpendicular to it, while being more elongated on steeper slopes, which is common to thermal contraction polygons. Although we cannot exclude that desiccation cracking plays a minor role for the formation of the here described polygons, we conclude that their genesis is dominated by thermal contraction.

Evaluating the capacity of single photon lidar for terrain characterization under a range of forest conditions

Accurate digital elevation models are key data products used to inform forest management. Light detection and ranging (lidar) technologies have emerged as a useful tool for acquiring detailed terrain information, although the accuracy of this data is known to vary with topographic complexity and the density and characteristics of overlying vegetation. Single Photon Lidar (SPL) provides a high-density point cloud that can be acquired from a much higher altitude than discrete return, small-footprint lidar (hereafter, linear-mode lidar or LML), providing efficiencies and potential cost savings for operational mapping programs. Herein, we assess the absolute and relative accuracies of leaf-on and leaf-off SPL data acquired at different altitudes for characterizing terrain under varying vegetation types and densities and compare to results for LML data. Our assessment was forest-focused and primarily point based, using 299 Real-Time Kinematic survey checkpoints to quantify elevation errors (Δh); however, we also investigated and reported accuracy for linear transects, and conducted a wall-to-wall comparison of the SPL-derived 1-m digital elevation models (DEMs) against an LML-derived DEM. Point cloud characteristics for the leaf-on 2018 SPL data were markedly different, with 88% of returns as first returns, compared to 17% for the LML, and 59% and 46% for the leaf-off SPL data acquired at 3800 m and 2000 m, respectively. Of the datasets considered herein, the SPL data acquired under leaf-on conditions in 2018 had the lowest accuracy and precision for characterizing terrain underneath vegetation cover, with an RMSE of 10.97 cm and a 95th quantile of 24.03 cm; however these values are within commonly accepted error limits for elevation products. The leaf-off SPL data were most accurate overall; however, the differences between the leaf-off SPL data acquired at 3800 m versus 2000 m were often minor (< 1 cm on average), with similar patterns in Δh between the two datasets (r = 0.8). In terms of the relative performance of the lidar datasets examined, results from the analyses of linear transects were similar to those of the checkpoints, but highlighted the variability in elevation accuracy within similar cover types. Wall-to-wall comparisons of the SPL-derived DEMs to the 2012 LML DEM also corroborated the results of the checkpoint assessment, with the 2018 SPL leaf-on DEM having the largest differences (mean difference = 7.44 cm; RMSD = 18.07 cm). Differences between DEMs did not trend consistently with increasing canopy cover or with the percentage of returns that were within ±15 cm of the ground surface. We found that it was not only the density of the vegetation, but also the composition and configuration of both the overstory and understory vegetation that influenced the accuracy with which the lidar characterized the terrain surface. Overall, our results indicated that leaf-on SPL is capable of capturing terrain information under a wide variety of forest and vegetation conditions, albeit at a lower accuracy than what is possible with leaf-on LML or leaf-off SPL, but at a level of accuracy that is within acceptable limits for most forest applications.

Landslide site delineation from geometric signatures derived with the Hilbert–Huang transform for cases in Southern Taiwan

Abstract Landslides are a frequently occurring threat to human settlements. Along with global climate change, the occurrence of landslides is the forecast to be even more frequent than before. Among numerous factors, topography has been identified as a correlated subject and from which hillslope landslide-prone areas could be analyzed. Geometric signatures, including statistical descriptors, topographic grains, etc., provide an analytical way to quantify terrain. Various published literature, fast Fourier transform, fractals, wavelets, and other mathematical tools were applied for this parameterization. This study adopts the Hilbert–Huang transform (HHT) method to identify the geomorphological features of a landslide from topographic profiles. The sites of the study are four “large-scale potential landslide areas” registered in the government database located in Meinong, Shanlin, and Jiasian in southern Taiwan. The topographic mapping was conducted with an airborne light detection and ranging instrument. The resolution of the digital elevation model is 1 m. Each topographic profile was decomposed into a number of intrinsic mode function (IMF) components. Terrain characterization was then performed with the spectrum resulting from IMF decomposition. This research found that the features of landslides, including main scarp-head, minor scarp, gully, and flank, have strong correspondence to the features in the IMF spectrum, mainly from the first and the second IMF components. The geometric signatures derived with HHT could contribute to the delineation of the landslide area in addition to other signatures in the terrain analysis process.

HYDROPOWER DAM SITE SELECTION AND VISUALIZATION USING GIS AND RS TECHNIQUES: A CASE OF MARINDUQUE, PHILIPPINES

Abstract. The energy supply of the Philippines is dwindling considering rapid population growth, thus the need to maximize the advantages of harnessing renewable energy (RE) and optimizing its utilization in the grid. One of the RE sources that is considered practical due to its availability is running water, thus the development of hydropower. The use of remote sensing (RS) datasets and geographic information system (GIS) techniques are useful for pre-feasibility studies of hydropower development. This study utilizes Interferometric Synthetic Aperture Radar (IFSAR) – derived DEM, GIS-based hydrology and terrain characterization tools to identify natural reservoirs, and spatial analysis identify site for possible dam development. The methodology consists of two processes: valley determination, a component of landform classification, and flow accumulation. Different valley determination algorithms are included in the comparison analysis such as Multi-Resolution Valley Bottom Flatness (MRVBF), Topographic Position Index (TPI), Valley and Ridge Detection (VRD) and Geomorphons, with the latter best describing the valleys within the Marinduque island. The identified valleys are intersected with sites having the most suitable elevation, slope and flow accumulation. The results of the study are different indicative sites for hydropower development, the volumetric capacity for which are generated given design specifications (e.g. different dam heights). Furthermore, upon computing the volume of water that the reservoir can contain, the reservoir design is represented as three-dimensional features over the terrain to visualize the dam development.

Evaluating tropical forest classification and field sampling stratification from lidar to reduce effort and enable landscape monitoring

In high biodiversity areas, such as the Amazon, forest inventory is a challenge due to large variations in vegetation structure and inaccessibility.Capturing the full gradient of variability requires the acquisition of a large number of sample plots. Pre-stratified inventory is an efficient strategy that reduces sampling effort and cost. Low-cost remote sensing techniques may significantly expand pre-stratification capacity; however, the simplest option, satellite optical imagery, cannot detect small variations in primary forests. Alternatively, three-dimensional information obtained from airborne laser scanning (ALS, a.k.a. airborne lidar) has been successfully used to estimate structural parameters in tropical forests. Our objective was to assess to what extent forest plot sampling effort could be reduced, while accurately estimating mean vegetation characteristics in the landscape, by stratifying with ALS structural properties, relative to a random, uniformed conventional approach. The study was developed in an 800-ha area of wet Amazonian forest (Acre, Brazil), including portions of palms, bamboo and dense forest. We estimated relevant structural attributes from ALS: canopy height, openness, rugosity and fractions of leaf area index (LAI) along the vertical profile. We clustered vegetation to define heterogeneity into structural types, employing the Ward method and Euclidean distance. Also, principal component analysis was employed to characterize the groups using field and ALS-derived structural attributes. We simulated sampling intensities to estimate the gain in reducing the field efforts based on pre-stratified and non-stratified forest inventory scenarios. The resulting stratification clearly distinguished the forest’s structural variation gradient and the vegetation density profile. For a fixed uncertainty of 10% in basal area estimation, the ALS-aided stratified inventory reduced the necessary number of field plots by 41%, relative to simple random sampling. The resulting reduction in sampling effort can offset the cost of ALS data collection, significantly enhancing its financial feasibility. In addition, ALS provides broad-coverage quantifications of basal area (or aboveground carbon stock), canopy structure, and accurate terrain characterization, which have an added value for forest management.

Terrain Awareness Using a Tracked Skid-Steering Vehicle With Passive Independent Suspensions.

This paper presents a novel approach for terrain characterization based on a tracked skid-steer vehicle with a passive independent suspensions system. A set of physics-based parameters is used to characterize the terrain properties: drive motor electrical currents, the equivalent track, the power spectral density for the vertical accelerations and motor currents. Based on this feature set, the system predicts the type of terrain that the robot traverses. A wide set of experimental results acquired on various surfaces are provided to verify the study in the field, proving its effectiveness for application in autonomous robots.

Mobility Enhancement of Heavy-Duty Tracked Vehicles Under Load Using Real-Time Terrain Characterization, Traction Control, and a Towing Winch

This paper presents a generalized, multibody dynamics model for a tracked vehicle equipped with a towing winch and control strategies that enhance vehicle mobility by regulating track slip based on real-time terrain characterization and automating winch use. The vehicle model is validated under conditions where no action is taken by the winch. Thereafter, two mobility enhancing control strategies are outlined. The first strategy regulates track slip to a real-time estimated value that generates maximum net traction. This is done by computing state-force estimates from a Kalman filter that are compared to terrain traction models using a Bayesian hypothesis selection approach. If the vehicle is traction limited and the first strategy fails, a second strategy that automates winch use is activated. Simulation results are shown for both scenarios.

평창동계올림픽 알파인 스키 활강ㆍ슈퍼대회전 코스의 지형 특성 및 경기력 요인에 미치는 영향

평창동계올림픽 알파인 스키 활강ㆍ슈퍼대회전 코스의 지형 특성 및 경기력 요인에 미치는 영향

평창동계올림픽 알파인 스키 회전ㆍ대회전 코스의 지형 특성 및 경기력 요인에 미치는 영향

평창동계올림픽 알파인 스키 회전ㆍ대회전 코스의 지형 특성 및 경기력 요인에 미치는 영향