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The research covered in this paper is related to numerical modeling of realistic threats. The final goal is to simulate the effects from the impact of an RPG-7 projectile on a GAZ-2975 Tigr infantry vehicle protected by an ERA. The fragmentation that results from the impact is studied. Leading up to the final model, several models are created, starting with an explosively driven cylinder to study fragmentation modeling. The next step was modeling the setup defined by NATO STANAG 4526 which was used to characterize the impact from an RPG-7. Based upon these results, a generic RPG-7 model was developed which impacted an ERA, modeled as two plates with a layer of high explosive (HE) between them. All models ran successfully to normal termination, and they showed how numerical tools can be used to describe and investigate these very complicated events. [ABSTRACT FROM AUTHOR]

Airport ground handling plays an essential role in shortening the turnaround time for better utilization of aircraft that can reduce the cost per flight. One of the key challenges for improving the efficiency of airport ground operations is to comprehensively simulate the ground handling processes to optimize and evaluate various operation strategies. In this work, we develop an agent-based simulator that specifically focuses on the whole process of airport ground handling from landing to takeoff. It allows different types of agents, i.e. aircraft, ground vehicles, operators, and dispatchers, to execute their own behaviors according to the ground operation characteristics. The interactions among different agents are also modeled according to the specific service agreements. We use the Shanghai Pudong International Airport (PVG) for the case study to demonstrate the interfaces and outputs of the simulator including the simulated flight information, the progress of ground handling services, airfield status, etc. With the developed simulator, we could not only reproduce the real-world operations of different types of services but also have enough potential to optimize and evaluate various airport ground operation strategies under a series of complex scenarios. [ABSTRACT FROM AUTHOR]

Due to the vastly different energy consumption between up-slope and down-slope, a path with the shortest length in a complex off-road terrain environment (2.5D map) is not always the path with the least energy consumption. For any energy-sensitive vehicle, realizing a good trade-off between distance and energy consumption in 2.5D path planning is significantly meaningful. In this paper, we propose a deep reinforcement learning-based 2.5D multi-objective path planning method (DMOP). The DMOP can efficiently find the desired path in three steps: (1) transform the high-resolution 2.5D map into a small-size map, (2) use a trained deep Q network (DQN) to find the desired path on the small-size map, and (3) build the planned path to the original high-resolution map using a path-enhanced method. In addition, the hybrid exploration strategy and reward-shaping theory are applied to train the DQN. The reward function is constructed with the information of terrain, distance, and border. The simulation results show that the proposed method can finish the multi-objective 2.5D path planning task with significantly high efficiency and quality. Also, simulations prove that the method has powerful reasoning capability that enables it to perform arbitrary untrained planning tasks. [ABSTRACT FROM AUTHOR]

The instantaneous centers of rotation (ICRs) for the two treads of skid-steer vehicles moving with low inertia on hard horizontal terrain almost remain with constant local coordinates, which allows to establish an equivalence with differential-drive locomotion. However, this significant kinematic relationship has not been analyzed yet on sloped ground. One relevant difficulty of studying ICR behavior on inclined terrain, even on a flat surface, is the continuous variation of pitch and roll angles while turning. To overcome this problem, this paper analyzes a dynamic simulation of a skid-steer vehicle on horizontal ground where gravity is substituted by an equivalent external force in such a way that pitch and roll are kept constant. Relevant tread ICR variations on inclined ground have been deduced, which have a significant impact on skid-steer kinematics. These new findings have been corroborated experimentally with a four-wheeled mobile robot that turns on an inclined plane.

Chemical and mechanical thinning processes have long been used in stone and pome fruit production. During the thinning of apple flowers, growers use chemicals to regulate the tree load. Hand thinning is applied after the June drop to prune trees with excess crop load. The process of thinning can be unpredictable especially in biennial bearing cultivars. Thus, incentives to optimise chemical usage and to reduce expensive manual labour is ever increasing. Ground based machine vision systems have grown in popularity in orchard management due to the level of detail as well as plant coverage they can inspect with. Additionally, unmanned aerial vehicles (UAV) -based remote sensing technology is becoming a popular non-invasive quality inspection solution. This work proposes a framework for combining UAV and ground based RGB image data to detect flowering intensity in a Dutch Elstar apple orchard. The framework, based on point cloud reconstruction, presents automatic point cloud handling techniques as well as automated unsupervised flowering intensity estimation methods. Two linear regression models based on unsupervised machine learning methods were trained and validated from the framework that estimate flowering intensity in the orchard with both models having R2 > 0.65, RRMSE < 20% and p-stat < 0.005 for the correlation between the image derived flower index and the flower cluster number counted in field. The proposed methods provide a novel strategy for guiding flower thinning using simple RGB images and location data only. Moreover, the proposed methods also reveal the flexibility of intra-tree inspection by checking its sub-volumes. • Automatic flower cluster estimation at the tree level with a R2 = 0.7, RMSE < 20. • Ground and aerial point clouds have better performance when combined. • Novel strategy developed for guiding flower thinning in orchard management. • Dataset with aerial and ground images, and field sampling data released. [ABSTRACT FROM AUTHOR]

Global luminescent oil-film skin friction meter is used to extract the skin friction topology on two generic ground vehicle models: the Ahmed body and the DrivAer model notchback configuration. Topological structures of skin friction on the top and side surfaces are extracted on both the models at different Reynolds numbers (2.68 × 105, 5.35 × 105, and 8.03 × 105 for the Ahmed body and 2.92 × 105 and 5.84 × 105 for the DrivAer model, corresponding to freestream velocities of 10, 20, and 30 m/s for the Ahmed body and 10 and 20 m/s for the DrivAer model). For the Ahmed body, important features such as forebody separation bubble, longitudinal vortex, and C-pillar vortices and vortex bursting on the rear slant angle are identified. Similarly, for the DrivAer model, in addition to separation and reattachment lines and vortex bursting, more complex vortex interactions in the rear window are revealed. Comparisons of the results obtained for both the models with existing flow visualizations and numerical results are discussed. [ABSTRACT FROM AUTHOR]

In the present study, aerodynamic properties of modified generic pickup trucks were investigated by means of finite volume method. Steady, three-dimensional and turbulent flows over the pickup trucks were solved by standard k-epsilon turbulence model. An experimentally investigated two-dimensional pickup truck found in the open literature was used as a benchmark case and some modifications were done on it by closing the sides of the bed first. Then a tonneau was used to close the top of the box and finally, a canopy was used to cover the box completely from the tailgate to the cab roof. Simulations reveal that such modifications that were done on the reference case improve the aerodynamic characteristics of the vehicles in terms of drag coefficient. With respect to the original case, the drag coefficient reduces approximately 50%, 30% and 20% by using a canopy, a tonneau and closing all sides except top of the bed. Such decreases in drag coefficient was achieved because every modification prevents the flow separation more effectively around the bed and behind the cab. Regardless of the shape of the bed, the drag coefficient decreases with increasing Reynolds (Re) number up to Re=120103. It seems that this is the critical Reynolds number since drag coefficient does not change considerably with Re any more.

Bu çalışmada, modifiye edilerek oluşurulmuş kamyonetlerin aerodinamik özellikleri sonlu hacimler metodu ile incelenmiştir. Kamyonetler etrafındaki daimi, üç-boyutlu ve türbülanslı akışlar standart k-epsilon türbülans modeli ile çözülmüştür. Açık literatürde bulunan ve deneysel olarak iki-boyutlu-olarak incelenmiş bir kamyonet referans alınarak, kasası üzerinde bir takım modifikasyonlar yapılmıştır. Bunun için, kasanın yanlarının dışında, üst kısmı da düzlemsel bir yüzeyle örtülmüştür. Son olarak, kasanın üstü, yanlarıyla beraber kamyonet kasasından kabin üst yüzeyine kadar her tarafından tamamen kapatılmıştır. Yapılan simülasyonlar, referans araç üzerinde yapılan değişikliklerin, direnç katsayısı dikkate alındığında, araçların aerodinamik karakteristiklerini iyileştirdiğini ortaya koymuştur. Orijinal duruma nazaran aracın kabinden kasanın üstüne kadar kaplanmasıyla %50, kasanın arka, üst ve yanlarının kapatılmasıyla %30 ve sadece kasanın arka ve yanlarının kapatılıp üst tarafının açık bırakılmasıyla direnç değerinde, %20 düşüş sağlanmıştır. Dirençteki bu azalmaların sebebi, kasa etrafında ve kasa ile kabin arasındaki bölgede akış ayrılmasının önlenmesidir. Kasanın şekli ne olursa olsun, direnç katsayısı Reynolds (Re) sayısının Re=120103‘e kadar arttırılmasıyla azalmıştır. Bu değerden sonra direnç değerleri Re sayısıyla artık çok fazla değişmediği için bu değerin kritik Reynolds sayısı olduğu görülmüştür.

Assessing the driving-style from dynamic data is a well established line of research, which has tackled the description of risky behaviours, the profiling of energy-consumption habits, and the detection of different driver’s characteristics from the analysis of motion data. In the last years, as smartphone ownership has become widespread, such an assessment has been increasingly relying on the measurements taken from the inertial sensors on board of the smartphone itself. This work stands in this context, and it aims to design a 4-dimensional driving-style assessment for insurance purposes. The main contribution is adding, to more common proxies of risky-driving, the dimension of smartphone usage, the detection of which is performed through an appropriate processing of smartphone-based inertial sensors, thus not relying on privacy-sensitive monitoring of phone usage behavior. Physics-based, fine-grained dynamic features are used to classify the overall riskiness of the driving-style, thus providing a comprehensive insight into the most discriminating features. The study is based on experimental data, collected over more than five thousands kilometers of varied car trips. [ABSTRACT FROM AUTHOR]

A framework for autonomous ground vehicle (AGV) path planning using global navigation satellite systems (GNSS) signals and cellular long-term evolution (LTE) signals is evaluated through several simulations and experiments. The objective of path planning is to prescribe the optimal path for the AGV to follow to reach a desired target point. Optimality is defined as the shortest distance, while minimizing the AGV's position estimation error and guaranteeing that the uncertainty about its position is below a desired threshold. Path planning is prescribed via signal reliability maps, which provide information about regions where large errors due to cellular signal multipath or obstructed GNSS line-of-sight are expected. Simulation results are presented demonstrating that utilizing ambient cellular LTE signals together with GNSS signals 1) reduces the uncertainty about the AGV's position, 2) increases the number of feasible paths to choose from, which could be useful if other considerations arise (e.g., traffic jams and road blockages due to construction), and 3) yields significantly shorter feasible paths, which would otherwise be infeasible with GNSS signals alone. Experimental results on a ground vehicle navigating in downtown Riverside, CA, USA, are presented demonstrating a close match between the simulated and experimental results. [ABSTRACT FROM AUTHOR]

Ground vehicles are the main sources of noise and infrasound in residential areas. Numerous published works are devoted to studies of audible sound, its sources in vehicle, and development of measures to its reduction. The authors believe that the problem of infrasound occurrence and propagation in residential areas and, hence, its negative impact on humans and ambient environment is underestimated. Development of predictions and experimental methods of studying infrasound generating mechanism and environmental monitoring of this negative factor are an urgent issue. Significant portion of acoustic emission from a vehicle is comprised for infrasound and low frequency range. Internal and external infrasound of vehicle has been measured. Prediction model has been proposed and the influence of vehicle design features on infrasound level has been estimated. Predictions of external infrasound generating mechanism of vehicles reveal that its frequency and level depend on driving speed, drag coefficient, vehicle body length, Strouhal number. Important sources of vehicle infrasound are wheels which execute rotation and progressive movement in airstream. Five wheel harmonics have been calculated for motor vehicles at various driving speeds, the existing dependences are detected. It has been demonstrated that the regulatory levels of infrasound can be exceeded only in residential areas adjacent to traffic flow with driving speeds higher than 80 km/h. The results of field studies of infrasound level on the streets of Moscow are presented. Recommendations on development of environmental monitoring and main approaches to reduce infrasound in urban environment are given

Vehicle sideslip angle is a major indicator of dynamics stability for ground vehicles; but it is immeasurable with commercially‐available sensors. Sideslip angle estimation has been the focus of intensive research in past decades, resulting in a rich library of related literature. This study presents a comprehensive evaluation of state‐of‐the‐art sideslip angle estimation methods, with the primary goal of quantitatively revealing their strengths and limitations. These include kinematics‐, dynamics‐ and neural network‐based estimators. A hardware‐in‐loop system is purposely established to examine their performance under four typical manoeuvres. The results show that the dynamics‐based estimators are suitable at low vehicle velocities when tires operate in the linear region. In contrast, the kinematics‐based methods yield superior estimation performance at high vehicle velocities, and the inclusion of the dual GPS receivers is beneficial even when there is large disturbance to the steering angle. Of utmost importance, it is experimentally manifested that the neural network‐based estimator can perform well in all manoeuvres once the training datasets are properly selected. [ABSTRACT FROM AUTHOR]

In the present study, aerodynamic properties of modified generic pickup trucks were investigated by means of finite volume method. Steady, three-dimensional and turbulent flows over the pickup trucks were solved by standard k-epsilon turbulence model. An experimentally investigated two-dimensional pickup truck found in the open literature was used as a benchmark case and some modifications were done on it by closing the sides of the bed first. Then a tonneau was used to close the top of the box and finally, a canopy was used to cover the box completely from the tailgate to the cab roof. Simulations reveal that such modifications that were done on the reference case improve the aerodynamic characteristics of the vehicles in terms of drag coefficient. With respect to the original case, the drag coefficient reduces approximately 50%, 30% and 20% by using a canopy, a tonneau and closing all sides except top of the bed. Such decreases in drag coefficient was achieved because every modification prevents the flow separation more effectively around the bed and behind the cab. Regardless of the shape of the bed, the drag coefficient decreases with increasing Reynolds (Re) number up to Re=120×103. It seems that this is the critical Reynolds number since drag coefficient does not change considerably with Re any more. [ABSTRACT FROM AUTHOR]

Guidance and control (G&C) technologies play a central role in the development and operation of vehicular systems. The emergence of computational guidance and control (CG&C) and highly efficient numerical algorithms has opened up the great potential for solving complex constrained G&C problems onboard, enabling higher level of autonomous vehicle operations. In particular, convex-optimization-based G&C has matured significantly over the years and many advances continue to be made, allowing the generation of optimal G&C solutions in real-time for many vehicular systems in aerospace, automotive, and other domains. In this paper, we review recent major advances in convex optimization and convexification techniques for G&C of vehicular systems, focusing primarily on three important application fields: (1) Space vehicles for powered descent guidance, small body landing, rendezvous and proximity operations, orbital transfer, spacecraft reorientation, space robotics and manipulation, spacecraft formation flying, and station keeping; (2) Air vehicles including hypersonic/entry vehicles, missiles and projectiles, launch/ascent vehicles, and low-speed air vehicles; and (3) Motion control and powertrain control of ground vehicles. Throughout the paper, we draw figures that illustrate the basic mission concepts and objectives, introduce key equations that characterize the feature of each class of problems and approaches, and present tables that summarize similarities and distinctions among the problems, ideas, and methods. Where available, we provide comparative analyses and reveal correlations between different applications and technical approaches. Finally, we identify open challenges and issues, discuss potential opportunities, and make suggestions for future research directions. [ABSTRACT FROM AUTHOR]

Ground vehicles are the main sources of noise and infrasounds in residential areas. Numerous published works are devoted to the studies of audible sound, its sources in vehicle, and development of measures to its reduction. The authors believe that the problem of infrasound occurrence and propagation in residential areas and - hence - its negative impact on humans and ambient environment is underestimated. Development of predictions and experimental methods of studying the infrasound generating mechanism and environmental monitoring of this negative factor are an urgent issue. A significant portion of acoustic emission from a vehicle comprises infrasound and low frequency range. Internal and external infrasound of vehicle were measured. A prediction model was proposed and the influence of vehicle design features on the infrasound level was estimated. Predictions of the external infrasound generating the mechanism of vehicles reveal that its frequency and level depend on driving speed, drag coefficient, vehicle body length, and Strouhal number Wheels, which execute rotation and progressive movement in airstream, are important sources of vehicle infrasound. Five wheel harmonics were calculated for motor vehicles at various driving speeds, the existing dependences are detected. It was demonstrated that the regulatory levels of infrasound can be exceeded only in residential areas adjacent to traffic flow with driving speeds higher than 80 km/h. The results of field studies of infrasound level on the streets of Moscow were presented. Recommendations on the development of environmental monitoring and main approaches to reduce infrasound in urban environment were given. [ABSTRACT FROM AUTHOR]

Optimal scheduling in an anti‐lock brake system of ground vehicles is performed through approximate dynamic programming for reducing the stopping distance in severe braking. The proposed optimal scheduler explicitly incorporates the hybrid nature of the anti‐lock brake system and provides a feedback solution with a negligible computational burden in control calculation. To this goal, an iterative scheme, called the value iteration algorithm, is used to derive the infinite horizon solution to the underlying Hamilton–Jacobi–Bellman equation. Performance of the proposed method in control of the brake system is illustrated using both linear‐in‐parameter neural networks and multi‐layer perceptrons. Simulation results demonstrate potentials of the method. [ABSTRACT FROM AUTHOR]

The problem associated with line formation (convoy) control among a team of aerial and ground vehicles is addressed by considering the fact that the vehicles share information with each other via communication channels that can be corrupted because of time delay. Among other results, we present rules for designing a decentralized control law that provides convoy stability, ensuring null formation error at steady state under the condition of constant speed, for the leader, compensating time-delay effects on the communication flow among the following vehicles. The results have been verified for convoy designs based on "look-ahead" topologies, in which each agent knows only the states of the preceding vehicles in the formation. The effectiveness of the proposed rules is finally illustrated by a simulation experiment involving a team of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs), executing a scouting mission. [ABSTRACT FROM AUTHOR]

Highlights • Comparison of eleven well-known machine learning algorithms. • Influence of tires, speed, soil, and mounting position of sensors. • Validation by means of a real planetary exploration rover (LATUV rover). • Deep discussion with the lessons learned. Abstract This paper presents a comprehensive comparison of well-known machine learning algorithms for estimating discrete slip events associated with individual wheels in planetary exploration rovers. This analysis is performed with various tuning configurations for each algorithm (55 setups). This research also shows the key role that environment plays in the performance of the learning algorithms: rover speed (0.05–0.25 [m/s]), type of terrain (gravel vs. sand), and tire type (off-road tires vs. smooth tires). These contributions are validated by using a broad data set collected using a planetary rover equipped with proprioceptive sensing. This work not only identifies the best algorithm to be deployed for discrete slip estimation, but it also helps with the selection and the mounting position of the sensing systems to be employed in future robotic planetary missions. [ABSTRACT FROM AUTHOR]

Abstract Classification of ground vehicles from the unmanned aerial vehicles (UAVs) is the basis for local precision strikes. In this paper, a method based on micro-Doppler effect which provides unique information of targets is proposed for ground vehicles classification. Firstly, models describing the air-to-ground relationship between ground vehicles and the UAV are built to derive mathematical expressions of radar echo signals. Secondly, Singular Value Decomposition (SVD) is utilized to analyze micro-Doppler components of the ground wheeled vehicle and the ground tracked vehicle respectively. Thirdly, according to classify requirements, specific signal components are restored by Singular Value Reconstruction (SVR). Seven micro-Doppler features are extracted from reconstructed signals. At last, these features are sent to Support Vector Machine (SVM) classifier. Compared with current related methods such as Empirical Mode Decomposition (EMD) and multi-level wavelet decomposition (MWD), experimental results in different cases illustrate the effectiveness of this method. Discrimination performance under various signal-to-noise ratios (SNRs) also proves the robustness of proposed method. [ABSTRACT FROM AUTHOR]

Reactivity is a key component for autonomous vehicles navigating on natural terrains in order to safely avoid unknown obstacles. To this end, it is necessary to continuously assess traversability by processing on-board sensor data. This paper describes the case study of mobile robot Andabata that classifies traversable points from 3D laser scans acquired in motion of its vicinity to build 2D local traversability maps. Realistic robotic simulations with Gazebo were employed to appropriately adjust reactive behaviors. As a result, successful navigation tests with Andabata using the robot operating system (ROS) were performed on natural environments at low speeds.

This article presents methods for estimating lateral vehicle velocity and tire cornering stiffness, which are key parameters in vehicle dynamics control, using lateral tire force measurements. Lateral tire forces acting on each tire are directly measured by load-sensing hub bearings that were invented and further developed by NSK Ltd. For estimating the lateral vehicle velocity, tire force models considering lateral load transfer effects are used, and a recursive least square algorithm is adapted to identify the lateral vehicle velocity as an unknown parameter. Using the estimated lateral vehicle velocity, tire cornering stiffness, which is an important tire parameter dominating the vehicle’s cornering responses, is estimated. For the practical implementation, the cornering stiffness estimation algorithm based on a simple bicycle model is developed and discussed. Finally, proposed estimation algorithms were evaluated using experimental test data.

A new simulation and processing methodology based on open source tools to produce high fidelity synthetic aperture radar (SAR) simulations of ground vehicles of varying types, as well as analysis of an applied automatic target recognition (ATR) technique is presented in this Letter. This work is based around the RaySAR open‐source model and the outputs have been configured for both monostatic and bistatic geometries. Input CAD models of various military and civilian vehicles are used to produce the SAR imagery. This output imagery was then used to train a tiny you only look once convolutional neural network (CNN) classifier. The classification success of the CNN applied was showed to produce significantly accurate results and the whole pipeline of processing enabled rapid evaluation of potential ATR methods against targets of choice. [ABSTRACT FROM AUTHOR]

In this study, the authors consider the problem of collision avoidance of ground vehicles. In this framework, they focus on a single vehicle, and they guarantee that a given safe set is not exited during a mission. More specifically, they consider the yaw‐lateral motion of a two‐wheel vehicle subject to a side wind, and they design a state‐feedback controller such that the vehicle follows the nominal trajectory with a given maximum error. For these purposes, they resort to a control system where a supervisor, implemented as a hybrid automata, decides to switch between two controllers, depending on the measured vehicle lateral displacement from the nominal trajectory. The two controllers are designed using an H∞ approach and the input–output finite‐time stability approach, respectively. Some simulation results are included, showing the effectiveness of the proposed architecture. [ABSTRACT FROM AUTHOR]

This paper presents the state-of-the-art and reviews the state-of-research of acoustic sensors used for a variety of navigation and guidance applications on air and surface vehicles. In particular, this paper focuses on echolocation, which is widely utilized in nature by certain mammals (e.g., cetaceans and bats). Although acoustic sensors have been extensively adopted in various engineering applications, their use in navigation and guidance systems is yet to be fully exploited. This technology has clear potential for applications in air and surface navigation/guidance for intelligent transport systems (ITS), especially considering air and surface operations indoors and in other environments where satellite positioning is not available. Propagation of sound in the atmosphere is discussed in detail, with all potential attenuation sources taken into account. The errors introduced in echolocation measurements due to Doppler, multipath and atmospheric effects are discussed, and an uncertainty analysis method is presented for ranging error budget prediction in acoustic navigation applications. Considering the design challenges associated with monostatic and multi-static sensor implementations and looking at the performance predictions for different possible configurations, acoustic sensors show clear promises in navigation, proximity sensing, as well as obstacle detection and tracking. The integration of acoustic sensors in multi-sensor navigation systems is also considered towards the end of the paper and a low Size, Weight and Power, and Cost (SWaP-C) sensor integration architecture is presented for possible introduction in air and surface navigation systems. [ABSTRACT FROM AUTHOR]

Line scanning cameras, which capture only a single line of pixels, have been increasingly used in ground based mobile or robotic platforms. In applications where it is advantageous to directly georeference the camera data to world coordinates, an accurate estimate of the camera's 6D pose is required. This paper focuses on the common case where a mobile platform is equipped with a rigidly mounted line scanning camera, whose pose is unknown, and a navigation system providing vehicle body pose estimates. We propose a novel method that estimates the camera's pose relative to the navigation system. The approach involves imaging and manually labelling a calibration pattern with distinctly identifiable points, triangulating these points from camera and navigation system data and reprojecting them in order to compute a likelihood, which is maximised to estimate the 6D camera pose. Additionally, a Markov Chain Monte Carlo (MCMC) algorithm is used to estimate the uncertainty of the offset. Tested on two different platforms, the method was able to estimate the pose to within 0.06 m/1.05° and 0.18 m/2.39°. We also propose several approaches to displaying and interpreting the 6D results in a human readable way. [ABSTRACT FROM AUTHOR]