Search Results (220)

Plant colonization patterns on deglaciated terrain give insight into the factors influencing alpine ecosystem development. Our objectives were to use a chronosequence, extending from the Little Ice Age (~1850) terminal moraine to the present glacier terminus, and biophysical predictors to characterize vegetation across Sperry Glacier’s foreland—a mid-latitude cirque glacier in Glacier National Park, Montana, USA. We measured diversity metrics (i.e., richness, evenness, and Shannon’s diversity index), percent cover, and community composition in 61 plots. Field observations characterized drainage, concavity, landform features, rock fragments, and geomorphic process domains in each plot. GIS-derived variables contextualized the plots’ aspect, terrain roughness, topographic position, solar radiation, and curvature. Overall, vegetation cover and species richness increased with terrain age, but with colonization gaps compared to other forelands, likely due to extensive bedrock and slow soil development, potentially putting this community at risk of being outpaced by climate change. Generalized linear models revealed the importance of local site factors (e.g., drainage, concavity, and process domain) in explaining species richness and Shannon’s diversity patterns. The relevance of field-measured variables over GIS-derived variables demonstrated the importance of fieldwork in understanding alpine successional patterns and the need for higher-resolution remote sensing analyses to expand these landscape-scale studies. Full article

Debris flows propagating in natural environments often encounter irregular terrain features, such as bottom roughness and man-made structures like groundsills, which significantly influence their behavior and dynamics. In practice, groundsills are commonly used as debris flow mitigation structures. This study examines the effects of a beam-type groundsill array on the flow behavior of sediment mixtures in an inclined channel using numerical simulations. The sediment mixtures, modeled as Bingham fluids, were tested as they flowed over groundsill arrays with varying densities, characterized by the spacing-to-height ratio ($d/h$) ranging from 2 to 10. The findings indicate that interaction with the groundsills produces a hydraulic jump-like flow, reaching a height approximately 2.2 times the approach flow depth across different array densities. High-density arrays ($d/h\le 4$) substantially hindered flow propagation, reducing front velocities but leading to sediment buildup upstream of the groundsills. Conversely, low-density arrays ($d/h>4$) facilitated smoother flow with higher velocities. These insights into the relationship between array density, flow behavior, and sediment trapping provide valuable guidance for optimizing groundsill array designs to effectively reduce the mobility of gravity-driven flows of non-Newtonian fluids (such as snow avalanches, debris, lava, or mudflows) and mitigate the associated risks. Full article

Locomotion over rough terrain is still a problem yet to be solved for legged robots. One of the problems arises from the inability to identify terrain roughness during locomotion, which could be crucial for decision-making and successful task completion. Our proposed terrain roughness method is inspired by the observation that humans can sense their limb position in space without looking at them, which allows us to estimate obstacle heights. This method is based on robot feet coordinate standard deviation (further referred to as $SD$) parameter evaluation. $SD$ values could be categorized to represent different terrain roughness, and such categories could be useful for selecting different gaits for different terrains. In this paper, we investigate the possibility of using already known feet coordinates to evaluate terrain roughness by calculating their standard deviation ($SD$). We present simulation results that show that the $SD$ value only depends on terrain roughness and is not influenced by large terrain slopes. Experiments were conducted with real robots while walking over obstacles with different gaits to validate the method. This research mainly aims to test how robot gaits influence $SD$ parameters for terrain roughness evaluation. The experimental results showed that the $SD$ parameter calculated from the robot’s foot coordinates can be used to evaluate terrain roughness. The robot’s gaits have little to no influence on the $SD$ parameter. Full article

The body structures and motion stability of worm-like and snake-like robots have garnered significant research interest. Recently, innovative serial–parallel hybrid segmented robots have emerged as a fundamental platform for a wide range of motion modes. To address the hyper-redundancy characteristics of these hybrid structures, we propose a novel caterpillar-inspired Stable Segment Update (SSU) gait generation approach, establishing a unified framework for multi-segment robot gait generation. Drawing inspiration from the locomotion of natural caterpillars, the segments are modeled as rigid bodies with six degrees of freedom (DOF). The SSU gait generation method is specifically designed to parameterize caterpillar-like gaits. An inverse kinematics solution is derived by analyzing the forward kinematics and identifying the minimum lifting segment, framing the problem as a single-segment end-effector tracking task. Three distinct parameter sets are introduced within the SSU method to account for the stability of robot motion. These parameters, represented as discrete hump waves, are intended to improve motion efficiency during locomotion. Furthermore, the trajectories for each swinging segment are determined through kinematic analysis. Experimental results validate the effectiveness of the proposed SSU multi-gait generation method, demonstrating the successful traversal of gaps and rough terrain. Full article

Legged robots, especially quadruped robots, are widely used in various environments due to their advantage in overcoming rough terrains. However, falling is inevitable. Therefore, the ability to overcome a falling state is an essential ability for legged robots. In this paper, we propose a method to fully recover a quadruped robot from a fall using a single-neural network model. The neural network model is trained in two steps in simulations using reinforcement learning, and then directly applied to AiDIN-VIII, a quadruped robot with 12 degrees of freedom. Experimental results using the proposed method show that the robot can successfully recover from a fall within 5 s in various postures, even when the robot is completely turned over. In addition, we can see that the robot successfully recovers from a fall caused by a disturbance. Full article

Recent technological advancements and evolving regulatory frameworks are catalysing the integration of renewable energy sources in construction equipment, with the objective of significantly reducing greenhouse gas emissions. The electrification of non-road mobile machinery (NRMM), particularly self-propelled Rough-Terrain Variable Reach Trucks (RTVRT) equipped with telescopic booms, presents notable stability challenges. The transition from diesel to electric propulsion systems alters, among other factors, the centre of gravity and the inertial matrix, necessitating precise load capacity determinations through detailed load charts to ensure operational safety. This paper introduces a virtual model constructed through multiphysics modelling utilising the bond graph methodology, incorporating both scalar and vector bonds to facilitate detailed interconnections between mechanical and hydraulic domains. The model encompasses critical components, including the chassis, rear axle, telescopic boom, attachment fork, and wheels, each requiring a comprehensive three-dimensional treatment to accurately resolve spatial dynamics. An illustrative case study, supported by empirical data, demonstrates the model’s capabilities, particularly in calculating ground wheel reaction forces and analysing the hydraulic self-levelling behaviour of the attachment fork. Notably, discrepancies within a 10% range are deemed acceptable, reflecting the inherent variability of field operating conditions. Experimental analyses validate the BG-3D simulation model of the telehandler implemented in 20-SIM establishing it as an effective tool for estimating stability limits with satisfactory precision and for predicting dynamic behaviour across diverse operating conditions. Additionally, the paper discusses prospective enhancements to the model, such as the integration of the virtual vehicle model with a variable inclination platform in future research phases, aimed at evaluating both longitudinal and lateral stability in accordance with ISO 22915 standards, promoting operator safety. Full article

This article delineates the enhancement of an autonomous navigation and obstacle avoidance system for a quadruped robot dog. Part one of this paper presents the integration of a sophisticated multi-level dynamic control framework, utilizing Model Predictive Control (MPC) and Whole-Body Control (WBC) from MIT Cheetah. The system employs an Intel RealSense D435i depth camera for depth vision-based navigation, which enables high-fidelity 3D environmental mapping and real-time path planning. A significant innovation is the customization of the EGO-Planner to optimize trajectory planning in dynamically changing terrains, coupled with the implementation of a multi-body dynamics model that significantly improves the robot’s stability and maneuverability across various surfaces. The experimental results show that the RGB-D system exhibits superior velocity stability and trajectory accuracy to the SLAM system, with a 20% reduction in the cumulative velocity error and a 10% improvement in path tracking precision. The experimental results also show that the RGB-D system achieves smoother navigation, requiring 15% fewer iterations for path planning, and a 30% faster success rate recovery in challenging environments. The successful application of these technologies in simulated urban disaster scenarios suggests promising future applications in emergency response and complex urban environments. Part two of this paper presents the development of a robust path planning algorithm for a robot dog on a rough terrain based on attached binocular vision navigation. We use a commercial-of-the-shelf (COTS) robot dog. An optical CCD binocular vision dynamic tracking system is used to provide environment information. Likewise, the pose and posture of the robot dog are obtained from the robot’s own sensors, and a kinematics model is established. Then, a binocular vision tracking method is developed to determine the optimal path, provide a proposal (commands to actuators) of the position and posture of the bionic robot, and achieve stable motion on tough terrains. The terrain is assumed to be a gentle uneven terrain to begin with and subsequently proceeds to a more rough surface. This work consists of four steps: (1) pose and position data are acquired from the robot dog’s own inertial sensors, (2) terrain and environment information is input from onboard cameras, (3) information is fused (integrated), and (4) path planning and motion control proposals are made. Ultimately, this work provides a robust framework for future developments in the vision-based navigation and control of quadruped robots, offering potential solutions for navigating complex and dynamic terrains. Full article

In an investigation into how wind turbine noise interacts with the surrounding terrain, its propagation over rough ground is simulated using a parabolic equation code using a modified effective impedance model, which characterizes the effects of a three-dimensional, rigid roughness within a relatively long wavelength limit ($\mathrm{k}\mathrm{a}\le 1$). The model is validated by comparison to experiments conducted within an anechoic chamber wherein different source–receiver geometries are considered. The relative sound pressure level spectra from the parabolic equation code using the modified effective impedance model highlight a sensitivity to the roughness parameters. At a low frequency and far distance, the relative sound pressure level decreased as the roughness coverage increased. A difference of 4.9 dB has been reported. The simulations highlight how the roughness shifts the ground effect dips, resulting in the sound level at the distance of 2 km being altered. However, only the monochromatic wave has been discussed. Further work on broadband noise is desirable. Furthermore, due to the long wavelength limit, only a portion of audible wind turbine noise can be investigated. Full article

Surface roughness, interpreted in the wide sense of surface texture, is a generic term referring to a variety of aspects and scales of spatial variability of surfaces. The analysis of solid earth surface roughness is useful for understanding, characterizing, and monitoring geomorphic factors at multiple spatiotemporal scales. The different geomorphic features characterizing a landscape exhibit specific characteristics and scales of surface texture. The capability to selectively analyze specific roughness metrics at multiple spatial scales represents a key tool in geomorphometric analysis. This research presents a simplified geostatistical approach for the multiscale analysis of surface roughness, or of image texture in the case of images, that is highly informative and interpretable. The implemented approach is able to describe two main aspects of short-range surface roughness: omnidirectional roughness and roughness anisotropy. Adopting simple upscaling approaches, it is possible to perform a multiscale analysis of roughness. An overview of the information extraction potential of the approach is shown for the analysis of a portion of the Taklimakan desert (China) using a 30 m resolution DEM derived from the Copernicus Glo-30 DSM. The multiscale roughness indexes are used as input features for unsupervised and supervised learning tasks. The approach can be refined both from the perspective of the multiscale analysis as well as in relation to the surface roughness indexes considered. However, even in its present, simplified form, it can find direct applications in relation to multiple contexts and research topics. Full article

The reproduced planetary boundary layer (PBL) wind is commonly applied in downscaled simulations using commercial CFD codes with Reynolds-averaged Navier–Stokes (RANS) turbulence modeling. When using the turbulent inlets calculated by numerical weather prediction models (NWP), adjustments of the turbulence eddy viscosity closures and wall function formulations are concerned with maintaining the fully developed wind profiles specified at the inlet of CFD domains. The impact of these related configurations is worth discussing in engineering applications, especially when commercial codes restrict the internal modifications. This study evaluates the numerical performances of open-source OpenFOAM 2.3.0 and commercial Fluent 17.2 codes as supplementary scientific comparisons. This contribution focuses on the modified turbulence closures to incorporate turbulent profiles produced from mesoscale PBL parameterizations and the modified wall treatments relating to the roughness length. The near-ground flow features are evaluated by selecting the flat terrains and the classical Askervein benchmark case. The improvement in near-ground wind flow under the downscaled framework shows satisfactory performance in the open-source CFD platform. This contributes to engineers realizing the micro-siting of wind turbines and quantifying terrain-induced speed-up phenomena under the scope of wind-resistant design. Full article

In this paper, we propose an adaptive fuzzy pure pursuit trajectory tracking algorithm for autonomous unmanned ground vehicles (UGVs), addressing the challenges of accurate and stable navigation in complex environments. Traditional pure pursuit methods with fixed look-ahead distances struggle to maintain precision in dynamic and uneven terrains. Our approach uniquely integrates a fuzzy control algorithm that allows for real-time adjustments of the look-ahead distance based on environmental feedback, thereby enhancing tracking accuracy and smoothness. Additionally, we combine this with model predictive control (MPC) and whole-body motion control (WBC), where MPC forecasts future states and optimally adjusts control actions, while WBC ensures coordinated motion of the UGV, maintaining balance and stability, especially in rough terrains. This integration not only improves responsiveness to changing conditions but also enables dynamic balance adjustments during movement. The proposed algorithm was validated through simulations in Gazebo and real-world experiments on physical platforms. In real-world tests, our algorithm reduced the average trajectory tracking error by 45% and the standard deviation by nearly 50%, significantly improving stability and accuracy compared to traditional methods. Full article

This paper proposes an evaluating and mapping methodology of terrain traversability for off-road navigation of autonomous vehicles in unstructured environments. Terrain features are extracted from RGB images and 3D point clouds to create a traversal cost map. The cost map is then employed to plan safe trajectories. Bayesian generalized kernel inference is employed to assess unknown grid attributes due to the sparse raw point cloud data. A Kalman filter also creates density local elevation maps in real time by fusing multiframe information. Consequently, the terrain semantic mapping procedure considers the uncertainty of semantic segmentation and the impact of sensor noise. A Bayesian filter is used to update the surface semantic information in a probabilistic manner. Ultimately, the elevation map is utilized to extract geometric characteristics, which are then integrated with the probabilistic semantic map. This combined map is then used in conjunction with the extended motion primitive planner to plan the most effective trajectory. The experimental results demonstrate that the autonomous vehicles obtain a success rate enhancement ranging from 4.4% to 13.6% and a decrease in trajectory roughness ranging from 5.1% to 35.8% when compared with the most developed outdoor navigation algorithms. Additionally, the autonomous vehicles maintain a terrain surface selection accuracy of over 85% during the navigation process. Full article

Autonomous vehicles play a crucial role in three-dimensional transportation systems and have been extensively investigated and implemented in mining and other fields. However, the diverse and intricate terrain characteristics present challenges to vehicle traversability, including complex geometric features such as slope, harsh physical parameters such as friction and roughness, and irregular obstacles. The current research on traversability analysis primarily emphasizes the processing of perceptual information, with limited consideration for vehicle performance and state parameters, thereby restricting their applicability in path planning. A framework of traversability analysis and path planning methods for autonomous wheeled vehicles on rigid terrains is proposed in this paper for better traversability costs and less redundancy in path planning. The traversability boundary conditions are established first based on terrain and vehicle characteristics using theoretical methods to determine the traversable areas. Then, the traversability cost map for the traversable areas is obtained through simulation and segmented linear regression analysis. Afterward, the TV-Hybrid A* algorithm is proposed by redefining the path cost functions of the Hybrid A* algorithm through the simulation data and neural network method to generate a more cost-effective path. Finally, the path generated by the TV-Hybrid A* algorithm is validated and compared with that of the A* and Hybrid A* algorithms in simulations, demonstrating a slightly better traversability cost for the former. Full article

We propose a perturbative method to compute electromagnetic scattering from slightly rough dielectric surfaces, which leads to the same result as the usual Small Perturbation Method (SPM) in a surprisingly simple way. The proposed method is based on three pillars: the volumetric perturbative approach, the reciprocity theorem, and a proper approximation of the electric field within the perturbation volume, that we name Internal Field Approximation (IFA). The proposed new mathematical derivation of the SPM turns out to be much simpler and more concise than the classical one. In addition, being based on a volumetric perturbation approach, it has the potential of dealing in future with surface and volume scattering within a unitary framework, which is useful in modelling scattering from, e.g., vegetated soil, snow-covered terrain, and inhomogeneous soils. Therefore, although the presented result is mainly theoretical, it can have important applications in remote sensing. Full article

Rockfalls are natural geological phenomena characterized by the abrupt detachment and freefall descent of rock fragments from steep slopes. These events exhibit considerable variability in scale, velocity, and trajectory, influenced by the geological composition of the slope, the topography, and other environmental conditions. By employing advanced modeling techniques and terrain analysis, researchers aim to predict and control rockfall hazards to prevent casualties and protect properties in areas at risk. In this study, two rockfall events in the villages of Myloi and Platiana of Ilia prefecture were examined. The research was conducted by means of HY-STONE software, which performs 3D numerical modeling of the motion of non-interacting blocks. To perform this modeling, input files require the processing of base maps and datasets in a GIS environment. Stochastic modeling and 3D descriptions of slope topography, based on Digital Elevation Models (DEMs) without spatial resolution limitations, ensure multiscale analysis capabilities. Considering this capability, seven freely available DEMs, derived from various sources, were applied in HY-STONE with the scope of performing a large number of multiparametric analyses and selecting the most appropriate and efficient DEM for the software requirements. All the necessary data for the multiparametric analyses were generated within a GIS environment, utilizing either the same restitution coefficients and rolling friction coefficient or varying ones. The results indicate that finer-resolution DEMs capture detailed terrain features, enabling the precise identification of rockfall source areas and an accurate depiction of the kinetic energy distribution. Further, the results show that a correct application of the model to different DEMs requires a specific parametrization to account for the different roughness of the models. Full article