Real Terrain Research Articles

Numerical Modeling of the Collective Behavior of Scarab Beetles Transporting Dung Balls and Competing for Them on Complex Terrain

AbstractTo avoid competition for the same food resource, scarab beetles form dung ball and roll it away from the dung pile. They randomly choose direction which they strictly follow. According to previous results, the dynamic behavior of the beetle rolling dung ball is essentially defined by the complexity of the surface. The typical real surface is combined from a universal scale invariant (fractal) component and another component having a well‐defined characteristic scale. If the transported dung ball has a size comparable with the dimensions of peculiarities of the surface, it strongly influences the motion trajectory. On the flat terrain and at the absence of competitors, beetles manage to roll the ball along a nearly perfect straight path. However, even if the beetle is alone, on a more realistic terrain, the motion is more complex. The motion becomes much more complicated in the realistic situation, when the beetles complete for the balls. In this study, a numerical model is developed which combines 1) attraction of the beetles to the dung, (2) production of the balls of different sizes depending on the size/fitness of the animals, 3) the ball transportation on complex terrains, and 4) the competition between the beetles for already existing balls. A strong correlation between typical radius of the ball and the size of the relief minimums and the results of the competitions, as well as possible optimal strategies of the beetle behavior are found.

High-fidelity aeroelastic analyses of wind turbines in complex terrain: fluid–structure interaction and aerodynamic modeling

Abstract. This paper shows high-fidelity fluid–structure interaction (FSI) studies applied to the research wind turbine of the WINSENT (Wind Science and Engineering in Complex Terrain) project. In this project, two research wind turbines are going to be erected in the south of Germany in the WindForS complex-terrain test field. The FSI is obtained by coupling the CFD URANS–DES code FLOWer and the multiphysics FEM solver Kratos Multiphysics, in which both beam and shell structural elements can be chosen to model the turbine. The two codes are coupled in both an explicit and an implicit way. The different modeling approaches strongly differ with respect to computational resources, and therefore the advantages of their higher accuracy must be correlated with the respective additional computational costs. The presented FSI coupling method has been applied firstly to a single-blade model of the turbine under standard uniform inflow conditions. It could be concluded that for such a small turbine, in uniform conditions a beam model is sufficient to correctly build the blade deformations. Afterwards, the aerodynamic complexity has been increased considering the full turbine with turbulent inflow conditions generated from real field data, in both flat and complex terrains. It is shown that in these cases a higher structural fidelity is necessary. The effects of aeroelasticity are then shown on the phase-averaged blade loads, showing that using the same inflow turbulence, a flat terrain is mostly influenced by the shear, while the complex terrain is mostly affected by low-velocity structures generated by the forest. Finally, the impact of aeroelasticity and turbulence on the damage equivalent loading (DEL) is discussed, showing that flexibility reduces the DEL in the case of turbulent inflow, acting as a damper that breaks larger cycles into smaller ones.

Unknown terrain imaging with adaptive spatial resolution using UAV

Recently, Unmanned Aerial Vehicles (UAVs), or drones equipped with advanced cameras, have introduced a major revolution in surveying wide landscapes to collect ground data very fast. Capturing images of an unknown area with the desired resolution is of utmost importance for many real-life applications. For a UAV fitted with a given camera, its altitude from the ground surface primarily determines the spatial resolution of mapping. Hence, for an uneven terrain, it is a great challenge to maneuver the UAV dynamically to capture snapshots maintaining the desired spatial resolution throughout the area. This paper proposes a novel fully-automated technique for simple nadir drones to capture images of an unknown 3D terrain satisfying a predefined spatial resolution criterion. Also, an adaptive real-time flight path planning scheme is presented to minimize the distance traversed by the drone. A detailed performance evaluation study on real terrain data shows that the proposed technique can cover almost 97−99% of the area, maintaining the specified resolution with an almost 46−59% reduction in the path length compared to the conventional techniques.

Multi-Group Gorilla Troops Optimizer with Multi-Strategies for 3D Node Localization of Wireless Sensor Networks.

The localization problem of nodes in wireless sensor networks is often the focus of many researches. This paper proposes an opposition-based learning and parallel strategies Artificial Gorilla Troop Optimizer (OPGTO) for reducing the localization error. Opposition-based learning can expand the exploration space of the algorithm and significantly improve the global exploration ability of the algorithm. The parallel strategy divides the population into multiple groups for exploration, which effectively increases the diversity of the population. Based on this parallel strategy, we design communication strategies between groups for different types of optimization problems. To verify the optimized effect of the proposed OPGTO algorithm, it is tested on the CEC2013 benchmark function set and compared with Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA) and Artificial Gorilla Troops Optimizer (GTO). Experimental studies show that OPGTO has good optimization ability, especially on complex multimodal functions and combinatorial functions. Finally, we apply OPGTO algorithm to 3D localization of wireless sensor networks in the real terrain. Experimental results proved that OPGTO can effectively reduce the localization error based on Time Difference of Arrival (TDOA).

Realistic simulation of hydraulic erosion on slope

AbstractHydraulic erosion plays an important role in the formation of landform. Influenced by runoff, soil is destroyed, denuded, and transported to form a specific landform. The simulation of erosion can not only be used for the construction of realistic terrain, but also for evaluation in soil and water conservation, which has important research and application value. We construct a hydraulic erosion simulation framework on the basis of soil and water conservation's theory, which considers various factors in erosion, including: water flow, infiltration, transportation, erosion, ditch widening, deposition, and soil sliding. It is the first time to consider the generation time of runoff, soil fixation of plant roots, and ditch widening phenomenon in simulation. Using this method, we can simulate ditch evolution process from rill, shallow ditch, cut ditch to gully, which consist with the real process. Compared with real photos and data, our method has authenticity in both visual and theoretical aspects.

An Evaluation of Wind Turbine-Induced Topographic Change in the Offshore Intertidal Sandbank Using Remote Sensing-Constructed Digital Elevation Model Data

With the rapid development of wind power generation, many marine wind farms have been developed on the offshore intertidal sandbank (OIS) along the coastal regions of Jiangsu Province, China during the last decade. In order to quantitatively assess the stability of offshore wind turbines and their induced topographic changes on the OIS, a digital elevation model (DEM)-based analysis supported by satellite remote sensing is adopted in the present study. Taking the Liangsha OIS at the middle of Jiangsu coast, China as the research area, we first used an enhanced waterline method (EWM) to construct the 30 m resolution DEMs for the years 2014 and 2018 with the embedment of tidal creeks to effectively express the detailed characteristics of the micro-terrain. Then, a hypothetical sandbank surface discrimination method (HSSDM) was proposed. By comparing the height difference between the hypothetical and the real terrain surface during the operation period, the wind turbine-induced topographic change rate (TCR) was estimated from the DEM of 2018. The results show that 73.47% of the 49 wind turbines in the Liangsha OIS have an erosional/depositional balanced influence on the intertidal sand body, 8.16% show a weak depositional influence, and 18.36% lead to weak erosion. The average erosional depth, 58.6 cm, reached nearly 6% to 10% of the maximum possible erosion estimated by the hydrodynamic model. Furtherly, using two DEMs for the years 2014 and 2018, the topographic change depths at the location of wind turbines were calculated. By comparing the wind turbine-induced terrain change with the naturally erosional/depositional depths of the OIS, the average contribution rate caused by the wind turbines achieved 42.17%, which meant that the impact of wind turbines on terrain changes could not be ignored. This work shows the potential of utilizing satellite-based remote sensing to monitor topographic changes in the OIS and to assess the influence of morphological variations caused by wind turbines, which will be helpful for offshore wind farm planning and intertidal environment protection.

PTRM: Perceived Terrain Realism Metric

Terrains are visually prominent and commonly needed objects in many computer graphics applications. While there are many algorithms for synthetic terrain generation, it is rather difficult to assess the realism of a generated output. This article presents a first step toward the direction of perceptual evaluation for terrain models. We gathered and categorized several classes of real terrains, and we generated synthetic terrain models using computer graphics methods. The terrain geometries were rendered by using the same texturing, lighting, and camera position. Two studies on these image sets were conducted, ranking the terrains perceptually, and showing that the synthetic terrains are perceived as lacking realism compared to the real ones. We provide insight into the features that affect the perceived realism by a quantitative evaluation based on localized geomorphology-based landform features (geomorphons) that categorize terrain structures such as valleys, ridges, hollows, and so forth. We show that the presence or absence of certain features has a significant perceptual effect. The importance and presence of the terrain features were confirmed by using a generative deep neural network that transferred the features between the geometric models of the real terrains and the synthetic ones. The feature transfer was followed by another perceptual experiment that further showed their importance and effect on perceived realism. We then introduce Perceived Terrain Realism Metrics (PTRM), which estimates human-perceived realism of a terrain represented as a digital elevation map by relating the distribution of terrain features with their perceived realism. This metric can be used on a synthetic terrain, and it will output an estimated level of perceived realism. We validated the proposed metrics on real and synthetic data and compared them to the perceptual studies.

Deep learning for asteroids autonomous terrain relative navigation

The future of humanity in space will require, more and more frequently, proximity operations with unexplored celestial bodies. In particular, asteroids and comets will have a crucial role in the future space economy and exploration. These bodies are often characterized by unknown terrain maps and lack of navigation infrastructure; this makes autonomous navigation challenging to accomplish. In this context, visual matching algorithms are not able to perform navigation if the map and the images captured online by the probe differ significantly for illumination conditions, scaling or rotation. To overcome these issues, in this work, we propose a siamese convolutional neural network capable of image matching and a position retrieval system for reliable autonomous navigation. The system is robust to image noise, reusable on multiple terrains and landing sites, and it does not require deploying any additional hardware component. In this work, the OSIRIS-REx Nasa’s mission was taken as reference for defining the navigation requirements and a 3D model of Bennu has been built to render training data. The image matching capabilities of the system have been tested on one validation dataset made of rendered images and one made of real images provided by NASA’s OSIRIS-REx mission. Besides, realistic descent scenarios have been simulated to test the system navigation accuracy in simulated but realistic conditions, and to evaluate the error recovery capabilities of the developed system. The system achieved mission compliant navigation accuracy on both real and simulated terrain maps, showing remarkable generalization capability highlighting the generality of the proposed solution.

Numerical Investigation of Pollutant Transport in a Realistic Terrain with the SPH-SWE Method

A large amount of wastewater from industrial and urban residents enters rivers and lakes through the sewage outlet, causing a deterioration of water quality near the sewage outlet. The smoothed particle hydrodynamics (SPH) formulation based on the open-source Fortran code SPHysics is extended to solve the advective diffusion for the evolution of the pollution distribution with the shallow water equation (SWE). Several numerical cases, such as the uniform flow and dam-break flows in one and two dimensions, are studied to verify the accuracy of the present SPH-SWE diffusion model. The results are in good agreement with the analytical solutions. The concentration of the negative value and oscillation could be avoided. It demonstrates that the current SPH-SWE diffusion model has good stability and reliability for solving the pollutant transport equation. The actual terrain case is also simulated to predict the concentration distribution of the river. The concentration is distributed in the center of the Nanmenxia River, where the flow velocity is relatively high. The simulation results are reasonable, implying that it has a high potential in predicting the diffusion process of pollutants in the actual terrain.

A novel approach for wind farm micro-siting in complex terrain based on an improved genetic algorithm

This study presents a novel approach for wind farm micro-siting in complex terrain based on an improved Genetic Algorithm (GA). Firstly, computational fluid dynamics (CFD) simulations validated by wind tunnel tests, are combined with meteorological measurements to determine the distribution of wind energy potential and wind speed-up ratio. Then, an improved GA considering the wind power density and topographic acceleration effects is proposed for the layout optimization of wind turbines. In this improved algorithm, calculation of the objective functions of the population is programmed uniquely and parallelized to boost efficiency, and a variable mutation rate is adopted to avoid from local optimum. Finally, the proposed approach is applied for the wind farm micro-siting in a real terrain in Changsha, China. Performance of the proposed approach is compared with the Greedy algorithm and the original GA in the case of various wake models and objective functions, and the uncertainties of results are analyzed in-depth. The results indicate that the proposed approach independent of wake models and objective functions is more effective in terms of offering a reasonable wind turbine layout plan than the above two previously mentioned methods.

MLNav: Learning to Safely Navigate on Martian Terrains

We present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MLNav</i> , a learning-enhanced path planning framework for safety-critical and resource-limited systems operating in complex environments, such as rovers navigating on Mars. MLNav makes judicious use of machine learning to enhance the efficiency of path planning while fully respecting safety constraints. In particular, the dominant computational cost in such safety-critical settings is running a model-based safety checker on the proposed paths. Our learned search heuristic can simultaneously predict the feasibility for all path options in a single run, and the model-based safety checker is only invoked on the top-scoring paths. We validate in high-fidelity simulations using both real Martian terrain data collected by the Perseverance rover, as well as a suite of challenging synthetic terrains. Our experiments show that: (i) compared to the baseline ENav path planner on board the Perserverance rover, MLNav can provide a significant improvement in multiple key metrics, such as a 10x reduction in collision checks when navigating real Martian terrains, despite being trained with synthetic terrains; and (ii) MLNav can successfully navigate highly challenging terrains where the baseline ENav fails to find a feasible path before timing out.

A Terrain Elevation Map Generation Method Based on Self-Attention Mechanism and Multifeature Sketch

To address the issues of low efficiency in manual terrain feature map annotating and poor realism in terrain elevation map generation, this paper proposes a terrain elevation map generation method based on self-attention mechanism and multifeature sketch. Firstly, the proposed method extracts features from a terrain elevation map using an adaptive feature enhancement method. Afterwards, our method adds a self-attention mechanism to the generator and discriminator of conditional generative adversarial network to capture the global spatial features and generates a realistic terrain elevation map. Finally, a level of detail method is used to visualize the three-dimensional terrain, and an interactive terrain editing tool for roaming interaction is implemented. Experimental data show that the proposed method performs well in subjective visual performance and objective criteria and has obvious advantages over other current typical methods.

Fast Path Planning for Long-Range Planetary Roving Based on a Hierarchical Framework and Deep Reinforcement Learning

The global path planning of planetary surface rovers is crucial for optimizing exploration benefits and system safety. For the cases of long-range roving or obstacle constraints that are time-varied, there is an urgent need to improve the computational efficiency of path planning. This paper proposes a learning-based global path planning method that outperforms conventional searching and sampling-based methods in terms of planning speed. First, a distinguishable feature map is constructed through a traversability analysis of the extraterrestrial digital elevation model. Then, considering planning efficiency and adaptability, a hierarchical framework consisting of step iteration and block iteration is designed. For the planning of each step, an end-to-end step planner named SP-ResNet is proposed that is based on deep reinforcement learning. This step planner employs a double-branch residual network for action value estimation, and is trained over a simulated DEM map collection. Comparative analyses with baselines demonstrate the prominent advantage of our method in terms of planning speed. Finally, the method is verified to be effective on real lunar terrains using CE2TMap2015.

A Modular Generative Approach for Realistic River Deltas: When L-Systems and cGANs Meet

Procedural terrain generation aims to create topographically coherent landscapes with realistic terrain features. Realistic landscapes of our blue planet are not complete without river deltas; however, there is an insufficient advancement in the generation of landscapes with this terrain feature. Therefore, this paper presents a modular approach to generate landscapes focused on the river deltas features. The modular proposal initially creates skeletons of deltas using a stochastic L-system grammar; we include the guidelines for the rules design. In the first module, we propose three L-systems that automatically create delta skeletons using these guidelines. The second module constructs the coastline and the sedimentary lands for the delta skeleton. Finally, the third module uses conditional generative adversarial networks (cGANs) to create the corresponding digital elevation models (DEMs) and land surface images. The evaluation of our proposal includes visual comparisons, and image quality metrics: the Frechet Inception Distance (FID), and the Naturalness Image Quality Evaluator (NIQE). The proposed modular integration generates realistic deltas with enough variability to outperform related work.

Modeling Communication over Terrain for Realistic Simulation of Outdoor Sensor Network Deployments

Popular wireless network simulators have few available propagation models for outdoor Internet of Things applications. Of the available models, only a handful use real terrain data, yet an inaccurate propagation model can skew the results of simulations. In this article, we present TerrainLOS, a low-overhead propagation model for outdoor Internet of Things applications that uses real terrain data to determine whether two nodes can communicate. To the best of our knowledge, TerrainLOS is the first terrain-aware propagation model that specifically targets outdoor IoT deployments and that uses height maps to represent terrain. In addition, we present a new terrain classification method based on terrain “roughness,” which allows us to select a variety of terrain samples to demonstrate how TerrainLOS can capture the effects of terrain on communication. We also propose a technique to generate synthetic terrain samples based on “roughness.” Furthermore, we implemented TerrainLOS in the COOJA-Contiki network simulation/emulation platform, which targets IoT deployments and uses TerrainLOS to evaluate how often a network is fully connected based on the roughness of terrain, as well as how two popular power-aware routing protocols, RPL and ORPL, perform when terrain is considered.

Development of Smoothed Particle Hydrodynamics for Simulation of Flow and Contaminant Transport on Natural Urban Terrain and Streams

Smoothed Particle Hydrodynamics (SPH) method is proposed, as an alternative mesh-free approach to model all components of rainfall, surface runoff, fluid flow and contaminant transport with the representation of contaminant and fluid, as particles. By doing so, contaminant particles can be traced for the motion within runoff or fluid flow, even in the form of minute concentration which is difficult to render in conventional Eulerian grid methods. Weakly compressible SPH (WCSPH) is selected with cubic spline kernel, and the incorporation of Large Eddy Simulation (LES) representing turbulence effect. Various SPH diffusion formulations have been reviewed and selected. The selected SPH formulation for contaminant concentration is validated against analytical diffusion equation with boundary conditions of solid wall or free surface. The validated method is applied to calculate the overland flow and the contaminant transport on a model terrain and a real terrain geometry. The real terrain is a part of the city of Teluk Intan in Perak, Malaysia and is simulated using digital elevation model (DEM) data retrieved from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Water Body Dataset (ASTWBD) for ground elevation and channel surface.

Authoring multi-style terrain with global-to-local control

The appearance styles of natural terrains vary significantly from region to region in real world, and there is a strong need to effectively produce realistic terrain with certain style in computer graphics. In this paper, we advocate a novel neural network approach to the rapid synthesis of multi-style terrains that could directly learn and infer from real terrain data. The key idea is to explicitly devise a conditional generative adversarial network (GAN) which encourages and favors the maximum-distance embedding of acquired styles in the latent space. Towards this functionality, we first collect a dataset that exhibits apparent terrain style diversity in their style attributes. Second, we design multiple discriminators that can distinguish different terrain styles. Third, we employ discriminators to extract terrain features in different spatial scales, so that the developed generator can produce new terrains by fusing the finer-scale and coarser-scale styles. In our experiments, we collect 10 typical terrain datasets from real terrain data that cover a wide range of regions. Our approach successfully generates realistic terrains with global-to-local style control. The experimental results have confirmed our neural network can produce natural terrains with high fidelity, which are user-friendly to style interpolation and style mixing for the terrain authoring task.

Quantitative and Qualitative Terrain Analysis Based on Digital Terrain Model

Within the Digital Terrain Models (DTM) processing and consequently qualitative and quantitative analysis, it is possible to gain a credible imagination of real terrain shape. In order to obtain an appropriate DTM, it is necessary to decrease the influence of the gross errors that have negative effects on the final DTM. These gross errors may degrade and in the worst case also ruin the calculations and the final outputs. The gross errors have a greater impact and are harder to define in complicated terrain and pointing out these types of errors depends on the editor’s experiences and terrain knowledge.

Route Planning of Unmanned Aerial Vehicles under Recharging and Mission Time Constraints

This paper investigates the problem of route planning for rechargeable unmanned aerial vehicles (UAV) under the mission time constraint in cases where more than one trip per round is required due to limited battery capacities. The goal is to determine the number of UAVs to be deployed and the flying paths that minimize the total mission cost. Unlike previous works, the electric cost incurred by UAV recharging proportional to actual flying distances is incorporated into our model. The problem is formulated as a mixed-integer programming model to minimize the sum of electric charging cost, the UAV usage cost, and the penalty cost from the violation of the mission time constraint. Extensive numerical experiments are conducted to examine the integrity and performance of the proposed model under various model parameters and deployment scenarios in grid areas and a real terrain area. The optimal solutions can be obtained for small-scale problem instances in a reasonable runtime. For large-scale problems, only feasible solutions can be obtained due to limited computational resources.

Evaluation of Site Errors in LLS Magnetic Direction Finding Caused by Large Hills Using the 3D‐FDTD Technique

AbstractAngle and amplitude estimation errors in magnetic direction finding, called site errors, are important sensor‐specific errors in lightning location systems (LLS). They are known to be caused by nearby cables and overhead lines due to induced currents. Due to the reflection and diffraction of electromagnetic waves, hills and mountains are also expected to generate these effects. In this paper, numerical computations applying the finite‐difference time‐domain (FDTD) method are performed to analyze the impact of hills or mountains on the angle and amplitude estimation of LLS sensors for typical first and subsequent return strokes (RS). The influence of hill size, the distance of sensors to the hill, ground parameters, and sensor bandwidth, are evaluated. The results show that on top of low ridges of only 125 m elevation, up to ±10° angle site error and +50% amplitude site error occur. A key finding is that due to field attenuation caused by lossy ground and sensor bandwidth limitations, there is practically no difference in the angle site errors for first and subsequent RS. For two sensors, the average site errors obtained from real sensor measurements are compared to results from 3D‐FDTD simulations, modeling the real terrain based on a digital elevation model. The simulation results are in good agreement with the observed, average angle site errors.