Real Terrain Data Research Articles

The distributed strategy for asynchronous observations in data-driven wildland fire spread prediction

Background Asynchronous observations refer to observations that are obtained at multiple moments. The observation moments of fire fronts may differ throughout an entire wildfire area. Asynchronous observations include historical data, which hinders the effectiveness of data assimilation due to the lack of timely updates on changing fire fronts. Aims This paper proposed a distributed strategy combined with the Ensemble Transform Kalman filter (ETKF-distributed) for asynchronous observations. It can assimilate fire fronts immediately at any location by using new matching schemes between prediction and observation. Methods The ETKF-distributed undergoes testing using a wildland fire generated based on real terrain, vegetation, and historical weather data from the local area. In addition, the ETKF and ETKF-centralised proposed in our previous work were employed as comparisons. Observing System Simulation Experiments were conducted to generate asynchronous observation fire fronts. Key results The benefit of immediate assimilation enables the new method to maintain high accuracy predictions. Conclusions The allocation of observation resources can be focused in regions with high rates of speed when employing ETKF-distributed. Implications The ETKF-distributed has high efficiency and adaptability, making it highly promising for implementation in wildfire prediction.

Insights into the Movement and Diffusion Accumulation Characteristics of a Catastrophic Rock Avalanche Debris—A Case Study

In this study, the 1991 rock avalanche, in Touzhai, Zhaotong, Yunnan, China, was considered the study object. The investigation of the landslide accumulation body revealed that the Touzhai rock avalanche accumulation body has the characteristics of wide gradation and poor sorting. A combination of field investigations, indoor and outdoor experiments, and numerical simulations were used to invert the occurrence and spreading range of rock avalanche-debris flow hazards. To invert and analyze its dynamics and the crushing process, a three-dimensional discrete element modeling was performed on the real terrain data. Simulation results showed that the movement time of the numerically simulated Touzhai rock avalanche was approximately 200 s. After 50 s of movement, the peak velocity reached 32 m/s, and the velocity gradually decayed after the sliding mass rubbed violently against the valley floor and collided with the mountain. Due to the meandering nature of the gully, the sliding mass makes its way down the gully and constantly collides with the mountain, making particles appear to climb, with some particles being blocked by the valley. After 150 s of movement, the average velocity rate decreased substantially, and the landslide-avalanche debris reached the mouth of the trench. After 200 s of movement, the average sliding velocity tends to 0 m/s, where the avalanche debris tends to stop and accumulate. When the rock avalanche movement reaches the mouth of the gully, the avalanche debris spreads to the sides as it is no longer bounded by the hills on either side of the narrow gully, eventually forming a ‘trumpet-shaped’ accumulation, and the granular flow simulation matched the findings of the landslide site accumulation.

Sonic boom propagation over real topography

The effect of elevation variation on sonic boom reflection is investigated using real terrain data. To this end, the full two-dimensional Euler equations are solved using finite-difference time-domain techniques. Numerical simulations are performed for two ground profiles of more than 10 km long, extracted from topographical data of hilly regions, and for two boom waves, a classical N-wave, and a low-boom wave. For both ground profiles, topography affects the reflected boom significantly. Wavefront folding due to terrain depression is notably highlighted. For the ground profile with mild slopes, the time signals of the acoustic pressure at the ground are, however, only slightly modified compared to the flat reference case, and the associated noise levels differ by less than 1 dB. With steep slopes, the contribution due to wavefront folding has a large amplitude at the ground. This results in an amplification of the noise levels: a 3 dB increase occurs at 1% of the positions along the ground surface, and a maximum of 5–6 dB is reached near the terrain depressions. These conclusions are valid for the N-wave and low-boom wave.

Thermal Removal Method for Chang’e-5 Multispectral Data Using a Heat Conduction Model and Real Terrain

The lunar mineral spectrometer (LMS) onboard the Chang’e-5—China’s lunar exploration mission—collected in-situ multispectral data up to 3 μm over a large area of the lunar surface for the first time. As Chang’e-5 operated near the Moon’s noon local time, the spectral data are influenced by thermal radiation. In particular, the LMS multispectral data are in the form of characteristic spectral bands with discontinuities that make it difficult to directly apply existing empirical models for thermal removal. Here, we present a thermal removal method based on the one-dimensional heat conduction model and real terrain data. A centimeter-scale temperature distribution map of the multispectral measurement region is calculated, which ranged from 330 to 362 K. We calculated the temperatures of four hyperspectral regions using an empirical model, compared them with those of the same region using the proposed method, and obtained a relative error of less than 1%. Afterwards, we removed the effects of thermal radiation in the multispectral data using a thermal removal model with the temperature of the multispectral region. We compared multispectral data after thermal removal using our proposed method with hyperspectral data after thermal removal using an empirical model at similar locations. The results showed good consistency between the data corrected by both methods. This paper proposed method does not depend on the setting of the spectral band, it can provide a reference for thermal removal of Chang’e-5 multispectral data and that of subsequent in-situ non-continuous spectral detection data.

Computationally efficient and sub-optimal trajectory planning framework based on trajectory-quality growth rate analysis.

A planetary exploration rover has been used for scientific missions or as a precursor for a future manned mission. The rover's autonomous system is managed by a space-qualified, radiation-hardened onboard computer; hence, the processing performance for such a computer is strictly limited, owing to the limitation to power supply. Generally, a computationally efficient algorithm in the autonomous system is favorable. This study, therefore, presents a computationally efficient and sub-optimal trajectory planning framework for the rover. The framework exploits an incremental search algorithm, which can generate more optimal solutions as the number of iterations increases. Such an incremental search is subjected to the trade-off between trajectory optimality and computational burden. Therefore, we introduce the trajectory-quality growth rate (TQGR) to statistically analyze the relationship between trajectory optimality and computational cost. This analysis is conducted in several types of terrain, and the planning stop criterion is estimated. Furthermore, the relation between terrain features and the stop criterion is modeled offline by a machine learning technique. Then, using the criterion predicted by the model, the proposed framework appropriately interrupts the incremental search in online motion planning, resulting in a sub-optimal trajectory with less computational burden. Trajectory planning simulation in various real terrain data validates that the proposed framework can, on average, reduce the computational cost by 47.6% while maintaining 63.8% of trajectory optimality. Furthermore, the simulation result shows the proposed framework still performs well even though the planning stop criterion is not adequately predicted.

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.

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.

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.

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.

A Novel Three-Dimensional Path Planning Method for Fixed-Wing UAV Using Improved Particle Swarm Optimization Algorithm

This paper proposed an improved particle swarm optimization (PSO) algorithm to solve the three-dimensional problem of path planning for the fixed-wing unmanned aerial vehicle (UAV) in the complex environment. The improved PSO algorithm (called DCA ∗ PSO) based dynamic divide-and-conquer (DC) strategy and modified A ∗ algorithm is designed to reach higher precision for the optimal flight path. In the proposed method, the entire path is divided into multiple segments, and these segments are evolved in parallel by using DC strategy, which can convert the complex high-dimensional problem into several parallel low-dimensional problems. In addition, A ∗ algorithm is adopted to generated an optimal path from the particle swarm, which can avoid premature convergence and enhance global search ability. When DCA ∗ PSO is used to solve the large-scale path planning problem, an adaptive dynamic strategy of the segment selection is further developed to complete an effective variable grouping according to the cost. To verify the optimization performance of DCA ∗ PSO algorithm, the real terrain data is utilized to test the performance for the route planning. The experiment results show that the proposed DCA ∗ PSO algorithm can effectively obtain better optimization results in solving the path planning problem of UAV, and it takes on better optimization ability and stability. In addition, DCA ∗ PSO algorithm is proved to search a feasible route in the complex environment with a large number of the waypoints by the experiment.

On the application of radio planning tools in open environments for the improvement of autoguidance systems used in precision agriculture

Real Time Kinematic (RTK) systems, together with Global Navigation Satellite System (GNSS) networks, are real-time location systems that are widely used in precision agriculture for autoguidance systems in farm vehicles. The RTK uses a reference signal from a base station, and the accuracy of the autoguidance system depends strongly on the signal quality arriving at the receiver that is installed in the vehicle or rover. In this paper, the relationship between the accuracy of the autoguidance systems and the propagation losses between the vehicle and the reference station is studied. The Covermap software tool, which was developed by the authors and validated with measurements, is used to determine the propagation losses. The advantage of this tool is that it allows analyzing any environment because it uses real geographical terrain data obtained from OpenStreetMap. In addition, the results are obtained instantaneously. A study was performed to analyze if the use of the Covermap tool in open environments improves the accuracy of guidance systems. In the study, an agricultural vehicle was connected to different reference stations, also called CORS (Continuously Operating Reference Stations), near the work location. The relationship between the propagation losses predicted by Covermap and the vehicle’s drift when connecting to each of these stations was determined. The stations that presented lower propagation losses resulted in less drift. This kind of software tools are very useful for determining the best reference station to which the rover should connect at any moment not according to the criterion of proximity but rather according to propagation losses.

Learning-Based End-to-End Path Planning for Lunar Rovers with Safety Constraints.

Path planning is an essential technology for lunar rover to achieve safe and efficient autonomous exploration mission, this paper proposes a learning-based end-to-end path planning algorithm for lunar rovers with safety constraints. Firstly, a training environment integrating real lunar surface terrain data was built using the Gazebo simulation environment and a lunar rover simulator was created in it to simulate the real lunar surface environment and the lunar rover system. Then an end-to-end path planning algorithm based on deep reinforcement learning method is designed, including state space, action space, network structure, reward function considering slip behavior, and training method based on proximal policy optimization. In addition, to improve the generalization ability to different lunar surface topography and different scale environments, a variety of training scenarios were set up to train the network model using the idea of curriculum learning. The simulation results show that the proposed planning algorithm can successfully achieve the end-to-end path planning of the lunar rover, and the path generated by the proposed algorithm has a higher safety guarantee compared with the classical path planning algorithm.

PETOOL v2.0: Parabolic Equation Toolbox with evaporation duct models and real environment data

A new version of PETOOL (Parabolic Equation Toolbox) is introduced with various additional capabilities. PETOOL is an open-source and MATLAB-based software tool with a user-friendly graphical user interface (GUI) for the analysis and visualization of electromagnetic wave propagation over variable terrain and through arbitrary atmosphere. Four novel features of the second version are as follows: (i) Several evaporation duct models have been developed. (ii) Real atmosphere data have been included in the form of “Binary Universal Form for Representation (BUFR)” data developed by “World Meteorological Organization (WMO)”. (iii) Real terrain data have been incorporated into the toolbox in the form of “Digital Terrain Elevation Data (DTED)” developed by “National Imagery and Mapping Agency (NIMA)”. (iv) A special add-on has been developed to generate a 3D coverage map of propagation factor/loss on real terrain data. The toolbox can be used for research and/or educational purposes to analyze more realistic propagation scenarios in an easier and flexible manner. Program summaryProgram title: PETOOL v2.0 (Parabolic Equation Toolbox v2.0)CPC Library link to program files:http://dx.doi.org/10.17632/v8f42rn2zs.1Licensing provisions: GNU General Public License 3Programming language: MATLAB (MathWorks Inc.) R2019a. Partial Differential Toolbox, Curve Fitting Toolbox and Mapping Toolbox required.Journal Reference of previous version: O. Ozgun, G. Apaydin, M. Kuzuoglu, and L. Sevgi, PETOOL: MATLAB-based one-way and two-way split-step parabolic equation tool for radiowave propagation over variable terrain, Computer Physics Communications 182 (2011) 2638–2654.Does the new version supersede the previous version?: YesReasons for the new version: The new version of the toolbox has been enriched with several add-ons which allow the toolbox to make more realistic analyses with real terrain and atmospheric data. The toolbox has the ability to read real atmospheric data in the form of BUFR data and real terrain data in the form of DTED data. A wide range of evaporation duct models has been incorporated into the toolbox. A special add-on has been developed to generate a 3D coverage map of propagation factor/loss on real terrain data. Hence, the toolbox can be used to analyze more realistic propagation scenarios in an easier and flexible manner.Summary of revisions: (i) Several evaporation duct models have been included with real atmospheric data in BUFR format. (ii) Real terrain data in DTED format have been incorporated into the toolbox. (iii) A special add-on has been developed to generate a 3D coverage map of propagation factor/loss on real terrain data.Nature of problem: This program is designed with a user-friendly GUI for the analysis and visualization of radio-wave propagation over variable terrain on the Earth’s surface, and through homogeneous and inhomogeneous atmosphere by using real atmosphere and terrain data. It can easily model both horizontally- and vertically-varying atmospheric refraction (especially ducting) and multipath effects.Solution method: The program employs one-way and two-way Split-Step Parabolic Equation (SSPE) algorithms with a wide-angle propagator. The SSPE is an initial-value problem starting from a reference range (typically from an antenna), and marching out in range by obtaining the field along the vertical direction at each range step, through the use of step-by-step Fourier transformations. The two-way algorithm incorporates the backward-propagating waves into the standard one-way SSPE by utilizing an iterative forward–backward scheme for modeling multipath effects over a staircase-approximated terrain.

Shortest Path Planning for Energy-Constrained Mobile Platforms Navigating on Uneven Terrains

Finding a shortest feasible path between two given locations is a common problem in many real-world applications. Previous studies have shown that mobile platforms would consume excessive energy when moving along shortest paths on uneven terrains, which often consist of rapid elevation changes. Mobile platforms powered by portable energy sources may fail to follow such paths due to the limited energy available. This paper proposes a new heuristic search algorithm called constraints satisfying A* (CSA*) to find solutions to such resource constrained shortest path problems. When CSA* is guided by admissible heuristics, it guarantees to find a globally optimal solution to a given constrained search problem if such a solution exists. When CSA* is guided by consistent heuristics, it is optimally efficient over a class of equally informed admissible constrained search algorithms with respect to the set of paths expanded. Test results obtained using real terrain data verify the applicability of the proposed algorithm in shortest path planning for energy-constrained mobile platforms on uneven terrains.

Safe Exploration and Optimization of Constrained MDPs Using Gaussian Processes

We present a reinforcement learning approach to explore and optimize a safety-constrained Markov Decision Process(MDP). In this setting, the agent must maximize discounted cumulative reward while constraining the probability of entering unsafe states, defined using a safety function being within some tolerance. The safety values of all states are not known a priori, and we probabilistically model them via aGaussian Process (GP) prior. As such, properly behaving in such an environment requires balancing a three-way trade-off of exploring the safety function, exploring the reward function, and exploiting acquired knowledge to maximize reward. We propose a novel approach to balance this trade-off. Specifically, our approach explores unvisited states selectively; that is, it prioritizes the exploration of a state if visiting that state significantly improves the knowledge on the achievable cumulative reward. Our approach relies on a novel information gain criterion based on Gaussian Process representations of the reward and safety functions. We demonstrate the effectiveness of our approach on a range of experiments, including a simulation using the real Martian terrain data.

Traversability-Based RRT*for Planetary Rover Path Planning in Rough Terrain with LIDAR Point Cloud Data

Sampling-based search algorithms such as Rapidly-Exploring Random Trees (RRT) have been utilized for mobile robot path planning and motion planning in high dimensional continuous spaces. This paper presents a path planning method for a planetary exploration rover in rough terrain. The proposed method exploits the framework of a sampling-based search, the optimal RRT (RRT*) algorithm. The terrain geometry used for planning is composed of point cloud data close to continuous space captured by a light detection and ranging (LIDAR) sensor. During the path planning phase, the proposed RRT*algorithm directly samples a point (node) from the LIDAR point cloud data. The path planner then considers the rough terrain traversability of the rover during the tree expansion process of RRT*. This process improves conventional RRT*in that the generated path is safe and feasible for the rover in rough terrain. In this paper, simulation study on the proposed path planning algorithm in various real terrain data confirms its usefulness.

Performance Analysis of Reuse Distance in Cooperative Broadcasting

Cooperative broadcasting is a promising technique for robust broadcast with low overhead and delay in mobile ad hoc networks. The technique is attractive for mission-oriented mobile communication, where a majority of the traffic is of broadcast nature. In cooperative broadcasting, all nodes simultaneously retransmit packets. The receiver utilizes cooperative diversity in the simultaneously received signals. The retransmissions continue until all nodes are reached. After the packet has traveled a specific number of hops out from the source, denoted as reuse distance, the source node transmits a new broadcast packet in the time slot used for the previous broadcast packet. If the reuse distance is too small, interference causes packet loss in intermediate nodes. In the literature, a reuse distance of three is common. With an analysis based on a realistic interference model and real terrain data, we show that a reuse distance of at least four is necessary to avoid packet loss in sparsely connected networks, especially for high spectral efficiencies. For frequency hopping, widely used in military systems, we propose a novel method. This method almost eliminates interference for a reuse distance of three, increasing the throughput by 33% compared to systems with a reuse distance of four.

Chance-constrained dynamic programming with application to risk-aware robotic space exploration

Existing approaches to constrained dynamic programming are limited to formulations where the constraints share the same additive structure of the objective function (that is, they can be represented as an expectation of the summation of one-stage costs). As such, these formulations cannot handle joint probabilistic (chance) constraints, whose structure is not additive. To bridge this gap, this paper presents a novel algorithmic approach for joint chance-constrained dynamic programming problems, where the probability of failure to satisfy given state constraints is explicitly bounded. Our approach is to (conservatively) reformulate a joint chance constraint as a constraint on the expectation of a summation of indicator random variables, which can be incorporated into the cost function by considering a dual formulation of the optimization problem. As a result, the primal variables can be optimized by standard dynamic programming, while the dual variable is optimized by a root-finding algorithm that converges exponentially. Error bounds on the primal and dual objective values are rigorously derived. We demonstrate algorithm effectiveness on three optimal control problems, namely a path planning problem, a Mars entry, descent and landing problem, and a Lunar landing problem. All Mars simulations are conducted using real terrain data of Mars, with four million discrete states at each time step. The numerical experiments are used to validate our theoretical and heuristic arguments that the proposed algorithm is both (i) computationally efficient, i.e., capable of handling real-world problems, and (ii) near-optimal, i.e., its degree of conservatism is very low.

고도에 따른 렌더링 시스템을 위한 동적 컬링 방안

Dynamic culling scheme is usually implemented to handle overhead caused by rendering the massive large-scale terrain data in flight simulator. However, existing culling scheme without considering altitude is not suitable for flight simulator due to additional computational overhead. To solve this problem, in this paper, we propose hybrid approach by applying two dynamic culling schemes depending on altitude. In addition, we remove unnessary computational overhead by creating different z-map resolution when aircraft changes its altitude. The proposed scheme is implemented with open graphic library and tested with real terrain data. Through the experimental results, we can recognize the improved rendering speed about 8 to 73 percents as compared to existing scheme.

SINR Evaluation for Improved Practical Coordinated Multi-point Clustering

Coordinated multipoint (CoMP) networks cooperatively process user equipment (UE) to eliminate inter-cell interference. CoMP networks convert the interfering signals into useful information by controlling the interference among adjacent cells. This is important for UE at the cell edge. This paper examines deployment of CoMP networks and investigates how CoMP communications can enhance the signal-to-interference and noise ratio (SINR) in practical inter-site and intra-site networks. In addition, we propose weighted clustering according to the average number of active users in the sector. Priority in forming clusters is given to sectors with more users. This reduces the error caused by the uniform UEs distribution assumption used by many researchers. We consider real city topography, terrain data, and UEs distribution to evaluate the received power and estimate the SINR improvement in real CoMP networks.