Simulated Terrain Research Articles

Three-dimensional unmanned aerial vehicle path planning utilizing artificial gorilla troops optimizer incorporating combined mutation and quadratic interpolation operators

In real terrain and dynamic obstacle scenarios, the complexity of the 3D UAV path planning problem greatly increases. Thus, to procure the optimal flight path for UAVs in such scenarios, an augmented Artificial Gorilla Troops Optimizer, denoted as OQMGTO, is proposed. The proposed OQMGTO algorithm introduces three strategies: combination mutation, quadratic interpolation, and random opposition-based learning, aiming to enhance the ability to timely escape from local optimal path areas and rapidly converge to the global optimal path. Given the flight distance, smoothness, terrain collision, and other five realistic factors of UAVs, specific constraint conditions are proposed to address complex scenarios, aiming to construct a path planning model. By optimizing this model, OQMGTO algorithm solves the path planning problem in complex scenarios. The extensive validation of OQMGTO algorithm on CEC2017 test suite enhances its credibility as a powerful optimization tool. Comparison experiments are conducted in simulated terrain scenarios, including six multi-obstacle terrain scenarios and three dynamic obstacle scenarios. The experimental findings validate OOMGTO algorithm can assist UAV in searching for excellent flight paths, featuring high safety and reliability characteristics, which confirms the superiority of OOMGTO algorithm for path planning in simulated terrain scenarios. Furthermore, in four flight missions carried out in real terrains, OQMGTO algorithm demonstrates superior search performance, planning smooth trajectories without mountain collision.

Fine Calibration Method for Laser Altimeter Pointing and Ranging Based on Dense Control Points

Satellite laser altimetry technology, a novel space remote sensing technique, actively acquires high-precision elevation information about the Earth’s surface. However, the accuracy of laser altimetry can be compromised by alterations in the satellite-ground environment, thermal dynamics, and cosmic radiation. These factors may induce subtle variations in the installation and internal structure of the spaceborne laser altimeter on the satellite platform, diminishing measurement precision. In-orbit calibration is thus essential to enhancing the precision of laser altimetry. Through collaborative calculations between satellite and ground stations, we can derive correction parameters for laser pointing and ranging, substantially improving the accuracy of satellite laser altimetry. This paper introduces a sophisticated calibration method for laser altimeter pointing and ranging that utilizes dense control points. The approach interpolates discrete ground control point data into continuous simulated terrain using empirical Bayesian kriging, subsequently categorizing the data for either pointing or ranging calibration according to their respective functions. Following this, a series of calibration experiments are conducted, prioritizing “pointing” followed by “ranging” and continuing until the variation in the ranging calibration results falls below a predefined threshold. We employed experimental data from ground control points (GCPs) in Xinjiang and Inner Mongolia, China, to calibrate the GaoFen-7 (GF-7) satellite Beam 2 laser altimeter as per the outlined method. The calibration outcomes were then benchmarked against those gleaned from infrared laser detector calibration, revealing disparities of 1.12 s in the pointing angle and 2 cm in the ranging correction value. Post validation with ground control points, the measurement accuracy was refined to 0.15 m. The experiments confirm that the proposed calibration method offers accuracy comparable to that of infrared laser detector calibration and can facilitate the updating of 1:10,000 topographic maps utilizing stereo optical imagery. Furthermore, this method is more cost-effective and demands fewer personnel for ground control point collection, enhancing resource efficiency compared to traditional infrared laser detector calibration. The proposed approach surpasses terrain-matching limitations when calibrating laser ranging parameters and presents a viable solution for achieving frequent and high-precision in-orbit calibration of laser altimetry satellites.

Control of Autonomous Aerial Vehicles to Transport a Medical Supplies

Public health surveillance must guarantee the safety of people by limiting human mobility, in cases of isolation, through product deliveries, making it necessary to use drones to guarantee safety because they play a crucial role in several sectors. The literature review highlights the benefits of automation in-home delivery using drones, focusing on time efficiency and competitiveness in various sectors, and provides crucial design parameters to ensure its implementation in urban areas using different control techniques. A contribution was proposed to a solution that aims to realize the honeycomb design, which drones create during flight, controlled by a flight and delivery algorithm in a simulation environment applying an iterative methodology and continuous transport tests. medical burden. The results indicate a qualitative advance in the successful creation of simulated terrain, although the lack of numerical data on takeoffs and landings suggests the need for additional quantitative measurements. The current results support the efficiency of drones in route planning, precise management of medical cargo, and reduction of delivery time is numerical evidence that reinforces the robustness of the solution. In conclusion, this study developed a functional prototype to control drones with a flight planning algorithm and a swarm formation system for the transport of medical supplies in urban environments, although the need for future research to implement artificial intelligence technologies is noted. that improve transportation efficiency.

Learning Quadrupedal High-Speed Running on Uneven Terrain.

Reinforcement learning (RL)-based controllers have been applied to the high-speed movement of quadruped robots on uneven terrains. The external disturbances increase as the robot moves faster on such terrains, affecting the stability of the robot. Many existing RL-based methods adopt higher control frequencies to respond quickly to the disturbance, which requires a significant computational cost. We propose a control framework that consists of an RL-based control policy updating at a low frequency and a model-based joint controller updating at a high frequency. Unlike previous methods, our policy outputs the control law for each joint, executed by the corresponding high-frequency joint controller to reduce the impact of external disturbances on the robot. We evaluated our method on various simulated terrains with height differences of up to 6 cm. We achieved a running motion of 1.8 m/s in the simulation using the Unitree A1 quadruped. The RL-based control policy updates at 50 Hz with a latency of 20 ms, while the model-based joint controller runs at 1000 Hz. The experimental results show that the proposed framework can overcome the latency caused by low-frequency updates, making it applicable for real-robot deployment.

Intelligent terrain generation considering global information and terrain patterns

Simulated terrains can provide rich information for landform and terrain research, disaster prediction, rescue and disaster relief, and national security. Quickly generating an accurate simulated terrain for target areas is of great importance. However, for existing data-driven terrain generation methods, it is difficult to balance modeling accuracy and the amount of data required. To overcome this gap, this study proposes a deep learning method that integrates global information and pattern features of the local terrain (IGPN) to realize terrain generation. In our proposed IGPN method, we first apply both terrain patterns (ridge line and drainage line) and elevation points as input data; thus, the terrain features (i.e., channel and peak) and elevation of the target terrain can be provided. The local information extraction module (LIEM) and global information extraction module (GIEM) are then applied to generate local and global terrain features, respectively. Thus, global and local terrain features can be supplemented, and a more accurate terrain can be generated. Experiments show that the IGPN method performs state-of-the-art terrain-generation tasks. Specifically, compared with existing terrain generation methods (IETA, SRResNet, Bicubic, Kriging, FEN, and ERFFN), the terrain generated by IGPN is closer to the real terrain and can retain more local terrain features.

Experimental study on ice monitoring method for 10 kV transmission line with tangent tower in alpine landform

AbstractIce monitoring methods were applied for 110 kV and above transmission lines with tangent towers. However, the change in the vertical span is not considered, and a significant difference lies in the tower‐conductor structure of 10 kV transmission lines. For this reason, a proposal is made about the ice monitoring method for the 10 kV transmission line with tangent tower in alpine landform, which includes the ice monitoring system based on pressure measurement and corresponding equivalent ice thickness calculating methods. Different methods calculate the vertical span under different height difference coefficients. A finite element simulation model and a simulated ice load experiment system are established based on real conductors and insulators. Experiments and simulations under four simulated terrains are conducted within 2.5–20 mm ice thickness range. The comparison is made between this method and the method without considering the change in vertical span. The results show that the two methods are consistent and the relative errors are lower than ±4% in simulation and ±10% in experiment when the height difference coefficient is 0. When it is not 0, the relative errors of this method fall between +0.38% and +6.78% in simulation and −6.40% to +6.60% in experiment, while the relative errors of the method without considering the change in vertical span ranges between −11.13% and −20.23% in simulation and −11.65% to −23.20% in experiment.

Vision-Based Virtual Simulation Platform for Planetary Rovers

In order to evaluate the feasibility of planetary exploration missions, it is imperative to construct planetary terrain environments on the ground. Nevertheless, the implementation of this method is characterized by time-consuming and challenging factors. By contrast, resorting to simulation approaches represents a cost-effective and high-efficiency alternative, which can facilitate the simulation validation of planetary rover exploration missions in an effective manner. In this paper, a modular planet rover simulation platform is proposed; by building and overlaying multiple feature layers corresponding to the surface of the planet, we realize the simulation of high-resolution fine terrain and through adjusting the terrain parameters to meet the needs of different simulated terrain. The experimental results show that we have built a scene to satisfy the requirements of visual effects and physically realistic characteristics of simulation. Using an improved PatchMatch stereomethod to recover images captured real time by a planetary rover navigation camera in a virtual environment provided more complete three-dimensional terrain data for subsequent simulation validation of local path planning. Finally, a simulation environment that combines high-fidelity visual effects and kinematic characteristics supports visualizing the simulation platform: we propose a path planning method using global planning combined with local obstacle avoidance, and we obtain the optimal path that satisfies the kinematic constraints.

Photometry of LROC NAC resolved rock-rich regions on the Moon

The study and investigation of meter and sub-meter scale geological features, especially boulders and boulder fields, on the surface of airless bodies can provide insight into the evolution of the regolith and the contribution of various processes to its formation. Prior studies have examined the photometric properties of the lunar regolith surrounding young craters using image ratios. We extend this methodology to extracting surface properties, in particular the roughness characteristics, exclusive to boulder fields and the boulders that constitute them around impact craters. Our understanding of the response of boulders to space weathering, micrometeorite abrasion, thermal fatigue, and consequently their evolution into regolith can be improved by characterizing the surface roughness of the uppermost layer of boulders. In this study, rock-rich regions on the Moon are investigated using photometric roughness by employing a normalised logarithmic phase ratio difference metric to measure and compare the slope of the phase curve (reflectance versus phase angle) of a rock-rich field to a rock-free field. We compare the photometric roughness of rock-rich fields on simulated images with the photometric roughness of rock-rich fields on LROC NAC images (0.5 m/pixel). The simulated terrains are constructed with a set of geologically informed rock properties, i.e., rock size, shape, and size frequency distribution. The artificial terrains are then converted to reflectance images using the Hapke AMSA (Hapke, 2002) reflectance model. Using this technique, we determine that rock-rich surfaces are not necessarily photometrically rougher than rock-free areas. Additionally, we find the roughness of resolved rock fields to indicate the presence of diverse sub-mm scale rock roughness (microtopography) and, possibly, variable rock single scattering albedo. These latter properties are likely controlled by rock petrology and material response to weathering and erosion. Spatial clustering of photometrically smooth and rough boulder fields in the downrange and uprange of two craters is observed, reflecting ejecta asymmetry and possibly indicating asymmetric modification of ejecta rock surfaces during the impact excavation process.

Simulated topographic ventilation model based on phoenics analysis in CFD

As an effective process of passive energy saving, natural ventilation can not only use renewable energy to reduce non-renewable energy, but also improve indoor air quality, providing people with a healthier and more comfortable environment. With the gradual creation of sustainable development space, people pay more and more attention to energy saving, and natural ventilation is also regarded as a system and environmentally friendly ventilation technology. Natural ventilation is an important technical dimension in the construction of greenhouse today. The dual driving force of air pressure and thermal pressure can promote the generation of natural air, thus achieving global cooling, improve thermal comfort, and improve indoor air quality by blowing fresh air. The air pressure and the hot air pressure are always constant and unbalanced. Based on the scientific basis of near air pressure, the effect of the simulated ventilation model is analyzed using CFD phoenics software. Based on CFD numerical simulation technology, the typical structure is simulated on phoenics software platform, and some rules are obtained through analysis. The numerical model uses k-turbulence model, adopts the finite volume method for discrete, second order windward dispersion format, and the algorithm adopts SIMPLE algorithm coupled with speed and pressure. Finally, the outdoor building layout and the exterior window parameters of the typical rooms are obtained, so that the community building can meet the requirements of green ventilation. The experiment has proved that under the simulated terrain of this paper, the average number of indoor air ventilation can reach about 5 times, the air air age is small, and the air quality is improved.

Using Range Split-Spectrum Interferometry to Reduce Phase Unwrapping Errors for InSAR-Derived DEM in Large Gradient Region

The use of the conventional interferometric synthetic aperture radar (InSAR) to generate digital elevation models (DEMs) always encounters phase unwrapping (PU) errors in areas with a sizeable topographic gradient. Range split-spectrum interferometry (RSSI) can overcome this issue; however, it loses the spatial resolution of the SAR image. We propose the use of the RSSI-assisted In-SAR-derived DEM (RID) method to address this challenge. The proposed approach first applies the RSSI method to generate a prior DEM, used for simulating terrain phases. Then, the simulated terrain phases are subtracted from the wrapped InSAR phases to obtain wrapped residual phases. Finally, the residual phases are unwrapped by the minimum cost flow (MCF) method, and the unwrapped residual phases are added to the simulated phases. Both the simulated and TerraSAR-X data sets are used to verify the proposed method. Compared with the InSAR and RSSI methods, the proposed approach can effectively decrease the PU errors of large gradients, ensure data resolution, and guarantee the DEM’s accuracy. The root mean square error between the topographic phase simulated from the real DEM and the topographic phase generated from the proposed method is 2.22 rad, which is significantly lower than 6.60 rad for InSAR, and the improvement rate is about 66.36%.

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 and Experimental Analyses of Track Shoe Patterns for Vehicle on Sandy Terrain

A track is a vehicle propulsion system that consists of a continuous band of chains connected with track shoes or may be entirely made of rubber. The track system is still commonly used on various vehicles, including bulldozers, excavators, tanks, and tractors, and has recently been used in lunar expedition vehicles. A tracked vehicle is mainly designed to provide better mobility in rough, uneven, or slippery terrain. The main component of the track system is track shoes or so-called grousers. This track shoe geometry will determine the tractive performance of the vehicle. If an incorrect shape is used, the excavator will most likely slip. The previous study approached this traction problem by using the semi-empirical method. However, until now, the track geometry and traction relation are still unclear. This research focuses on the effects of track shoe geometry on tractive performance and vehicle climbing ability. The analysis was mainly conducted with a DEM (Discrete Element Method) simulation. The result from the experimental test is also confirmed using a small-scale RC prototype on simulated terrain with different slope variations. It is proven that the grouser height directly affects the climbing performance of tracked vehicles. A higher grouser height proves to be better at a higher inclination slope. However, for a low inclination angle, the flattened track was better. The traction result from the EDEM simulation also yields a similar result. However, higher usable traction means more friction is generated; therefore, the track with higher traction also takes more energy to cover the same distance.

Generating Terrain Data for Geomorphological Analysis by Integrating Topographical Features and Conditional Generative Adversarial Networks

With the development of artificial intelligence techniques for geographical knowledge discovery, simulated terrain generation based on deep-learning algorithms has become one practical way to construct accurate terrain data. However, it is still necessary to discuss whether the simulated topographic data contain the characteristics of specific landforms and can support related geographical studies. Therefore, in this study, a deep learning-based model inspired by previous research is constructed to generate loess landform data. We analyzed the influence of inputting different topographic features on terrain generation and evaluated the similarity between the simulated and reference data. The results show that the deep learning-based model can generate simulated topographic data that include similar elevation and slope probability distributions to the reference data of the loess landform. In addition, the generated results may have inaccurate terrain details, which can be regarded as noise in some cases. This indicates that the selection of input features should be carefully considered. Finally, the simulated data can subsequently support landform and terrain research, especially with intelligence algorithms that require large sets of topographic data.

Variation in the Polarization Loss Factor in an Unmanned Aerial Vehicle Jamming Link Due to the Attitude Change

A method to analyze the variation of the jamming power received by an unmanned aerial vehicle (UAV) receiver according to the change in the attitude of the small UAV is presented. The main interest of the study is in the variation of the polarization loss factor (PLF) in the UAV jamming link due to attitude change, which may be caused by the wind or intended movement. The attitude change is modeled by a combination of three successive rotations using roll, pitch, and yaw, which are defined as UAV rotations. The proposed method is applied to a jamming link, where a small UAV with an omnidirectional antenna is jammed by a jammer antenna on the ground. The method is applied to a plane ground and over a simulated terrain. The variation of the PLF according to the change of UAV attitude may be higher than the generally expected PLF at locations where the height difference between the UAV and the jammer is large, and near the locations where the jammer is located close to the vertical plane containing the rotation axis when the attitude change is represented by one rotation.

Icy road ahead-rapid adjustments of gaze-gait interactions during perturbed naturalistic walking.

Most humans can walk effortlessly across uniform terrain even when they do not pay much attention to it. However, most natural terrain is far from uniform, and we need visual information to maintain stable gait. Recent advances in mobile eye-tracking technology have made it possible to study, in natural environments, how terrain affects gaze and thus the sampling of visual information. However, natural environments provide only limited experimental control, and some conditions cannot safely be tested. Typical laboratory setups, in contrast, are far from natural settings for walking. We used a setup consisting of a dual-belt treadmill, 240 projection screen, floor projection, three-dimensional optical motion tracking, and mobile eye tracking to investigate eye, head, and body movements during perturbed and unperturbed walking in a controlled yet naturalistic environment. In two experiments (N = 22 each), we simulated terrain difficulty by repeatedly inducing slipping through accelerating either of the two belts rapidly and unpredictably (Experiment 1) or sometimes following visual cues (Experiment 2). We quantified the distinct roles of eye and head movements for adjusting gaze on different time scales. While motor perturbations mainly influenced head movements, eye movements were primarily affected by the presence of visual cues. This was true both immediately following slips and—to a lesser extent—over the course of entire 5-min blocks. We find adapted gaze parameters already after the first perturbation in each block, with little transfer between blocks. In conclusion, gaze–gait interactions in experimentally perturbed yet naturalistic walking are adaptive, flexible, and effector specific.

Numerical simulation of landscape evolution and mountain uplift history constrain—A case study from the youthful stage mountains around the central Hexi Corridor, NE Tibetan Plateau

Landscape evolution models (LEMs) are essential tools for analyzing tectonic-climate interactions and reproducing landform-shaping processes. In this study we used a LEM to simulate the evolution of the mountains from the central Hexi Corridor in the northeastern Tibetan Plateau, where the climate is arid and the surface processes are relatively uniform. However, there are pronounced differences in the topography between the mountains around the central Hexi Corridor. The East Jintanan Shan, West Jintanan Shan and Heli Shan are located in the northern part of the corridor; and the Yumu Shan in the southern part. Firstly, several representative areas were selected from these mountains to analyze the topographic characteristics, including the uniform valley spacing, local relief, and the outlet number. Secondly, a LEM for these areas was constructed using the Landlab platform, and the landscape evolution was simulated. With uniform valley spacing and other topographic characteristics as the criteria, we compared the realistic and simulated terrain for different model ages. Finally, based on the similarity of the simulated and realistic terrain, we estimated the timing of the initial uplift and the uplift rate of the four mountain ranges. The results are consistent with previous geological and geomorphological records from these youthful stage mountains that have not yet reached a steady state. Our findings demonstrate that LEMs combined with topographic characteristics are a reliable means of constraining the timing of the initial uplift and the uplift rate of the youthful stage mountain. Our approach can potentially be applied to other youthful stage mountains and it may become a valuable tool in tectonic geomorphology research.

Space Mining Robot Prototype for NASA Robotic Mining Competition Utilizing Systems Engineering Principles

The 2017 National Aeronautics and Space Ad-ministration (NASA) Robotic Mining Competition (RMC) is an outstanding opportunity for engineering students to imple-ment all the knowledge and experience that they gained in the undergraduate years, in building a robot that will provide an intellectual insight to NASA, to develop innovative robotic excavation concepts. For this competition, multiple universities from all over the U.S. will create teams of students and faculty member to design and build a mining robot that can traverse, mine, excavate at least 10 kg of regolith, then deposit it in a bin in the challenging simulated Martian terrain. Our team’s goal is to improve on our current design, and overcome DustyTRON 2.0’s limitations by analyzing them and implementing new engineering solutions. The process to improve this system will enable our team members to learn mechanical, electrical, and software engineering. DustyTRON 3.0 is divided into three sub-teams, namely, Mechanical, Circuitry, Software sub-teams. Mechanical team focused on solving the mechanical structure, robot mobility, stability and weight distribution. Circuitry team focused on the electrical components such as batteries, wiring, and motors. The Software team focused on programming the NVidia TK1, Arduino controller, and cameras integration. This paper will outline the detailed work following systems engineering principles to complete this project, from research, to design process and robot building compete at the Kennedy Space Center. Only 54 teams were invited to participate from allover the US and DustyTRON team represented the state of Texas and placed the 29th and awarded the “Innovative Design” award.

A Simple Yet Effective Whole-Body Locomotion Framework for Quadruped Robots.

In the context of legged robotics, many criteria based on the control of the Center of Mass (CoM) have been developed to ensure a stable and safe robot locomotion. Defining a whole-body framework with the control of the CoM requires a planning strategy, often based on a specific type of gait and a reliable state-estimation. In a whole-body control approach, if the CoM task is not specified, the consequent redundancy can still be resolved by specifying a postural task that set references for all the joints. Therefore, the postural task can be exploited to keep a well-behaved, stable kinematic configuration. In this work, we propose a generic locomotion framework which is able to generate different kind of gaits, ranging from very dynamic gaits, such as the trot, to more static gaits like the crawl, without the need to plan the CoM trajectory. Consequently, the whole-body controller becomes planner-free and it does not require the estimation of the floating base state, which is often prone to drift. The framework is composed of a priority-based whole-body controller that works in synergy with a walking pattern generator. We show the effectiveness of the framework by presenting simulations on different types of simulated terrains, including rough terrain, using different quadruped platforms.

DeepGait: Planning and Control of Quadrupedal Gaits Using Deep Reinforcement Learning

This letter addresses the problem of legged locomotion in non-flat terrain. As legged robots such as quadrupeds are to be deployed in terrains with geometries which are difficult to model and predict, the need arises to equip them with the capability to generalize well to unforeseen situations. In this work, we propose a novel technique for training neural-network policies for terrain-aware locomotion, which combines state-of-the-art methods for model-based motion planning and reinforcement learning. Our approach is centered on formulating Markov decision processes using the evaluation of dynamic feasibility criteria in place of physical simulation. We thus employ policy-gradient methods to independently train policies which respectively plan and execute foothold and base motions in 3D environments using both proprioceptive and exteroceptive measurements. We apply our method within a challenging suite of simulated terrain scenarios which contain features such as narrow bridges, gaps and stepping-stones, and train policies which succeed in locomoting effectively in all cases.

Flow and deposition measurement of foam beads in a closed re-circulating wind tunnel for snowdrift modelling

A facility was created for flow and deposition measurement of foam beads (Expanded Polystyrene) in a closed re-circulating wind tunnel to model the snowdrift experimentally. The geometrical and flow similarities between the model (foam beads) and prototype (snow) particles were established for the wind tunnel experiments. The wind tunnel was instrumented with the electronic regulator, anemometer and newly developed flux measuring sensors along with a data logger to carry out the experiments for different simulated terrain conditions. An array of optical sensors for measurement of foam beads flux was calibrated with the known quantity of the material and used to find the flux variation with wind speed inside the tunnel. All three modes of drifting transportation namely creeping, saltation and suspension could be observed through the transparent acrylic wall for data collection. The threshold velocity of foam bead movement was 4.05 m/s in the horizontal section and same was 2.7 m/s in the inclined section at an angle of 30° downward (a critical angle of avalanche starting zone). The process of snow deposition by the snow fence was simulated with the foam beads deposition around the scaled model (1:50) of a snow fence in the wind tunnel. The experiments helped to find a flow measuring technique for light drifting material like foam beads and the modelling of drifting snow.