This paper presents an online path planning approach for an autonomous tracked vehicle in a cluttered environment based on teaching–learning-based optimization (TLBO), considering the path ...smoothness, and the potential collision with the surrounding obstacles. In order to plan an efficient path that allows the vehicle to be autonomously navigated in cluttered environments, the path planning problem is solved as a multi-objective optimization problem. First, the vehicle perception is fully achieved by means of inertial measurement unit (IMU), wheels odometry, and light detection and ranging (LiDAR). In order to compensate the sensors drift to achieve more reliable data and improve the localization estimation and corrections, data fusion between the outputs of wheels odometry, LiDAR, and IMU is made through extended Kalman filter (EKF). Then, TLBO is proposed and applied to determine the optimum online path, where the objectives are to find the shortest path to reach the target destination, and to maximize the path smoothness, while avoiding the surrounding obstacles, and taking into account the vehicle dynamic and algebraic constraints. To check the performance of the proposed TLBO algorithm, it is compared in simulation to genetic algorithm (GA), particle swarm optimization (PSO), and a hybrid GA–PSO algorithm. Finally, real-time experiments based on robot operating system (ROS) implementation are conducted to validate the effectiveness of the proposed path planning algorithm.
Automatic detection of small objects such as vehicles in satellite images is a very challenging task, due to the complexity of the background, vehicles colors, the large size of ground sample ...distance (GSD) for satellite images and jamming caused by buildings and trees. Many methods were proposed for this task by using handcrafted features (such as a Histogram of an Oriented Gradient, Local Binary Pattern, Scale-Invariant Feature Transform, etc.) along with support vector machine classifier, however, Convolutional Neural Networks (CNN) have proved to be potentially more effective. In this paper, we use two advanced deep learning frameworks, Faster Region CNN (Faster R-CNN) and Single Shot Multi-Box (SSD) based on (CNN) with Inception-V2 as a feature map generator instead of VGG-16, to detect vehicles through Transfer Learning, and making an experimental analysis comparison between the two models. Experimental results on the test dataset demonstrate the effectiveness and efficiency of the proposed methods.
•Flexure and shear parameters for displacement of reinforced masonry shear walls.•Principle component analysis (PCA) and projection to latent space (PLS) methods.•Scoring models using 81 experimental ...reinforced masonry shear wall database.•Wall design characteristics influence on maximum displacements/drifts.
In the past decade, there has been an increased shift towards performance-based seismic design (PBSD) approaches to meet the requirements for the next generation of seismic codes worldwide. Displacement-based seismic design (DBSD) is key for implementing PBSD approaches as structural performance is typically linked to damage which in turn is associated with component displacements and deformations. Available reinforced masonry shear wall (RMSW) displacement prediction models in the literature are found to be unreliable when compared with published experimental results. This study outlines the use of a statistical multivariate analysis technique and applying it to develop a reliable model for the maximum displacement capacity prediction of RMSW systems. This approach is subsequently used to build scoring models based on an experimental database of 81 flexurally dominated RMSW tested under simulated seismic loads. The models are further utilized to investigate the influence of altering the wall design characteristics on their maximum displacement capacities. The developed models are considered a major step to facilitate DBSD codification of RMSW systems for the next generation of PBSD codes.
A new design is developed for compact-size and lightweight laser beam diffuser of two rad divergence (± 1 rad ≈ ± 60°). The design combines diffractive–refractive beam shaping optics, and it ...consists of a 532 nm military laser pointer, equipped with a laser dot matrix beam shaper and a rod lens. The non-Gaussian laser beam is spread into a relatively homogenous large-field illumination. This design is a low-cost optical configuration, which enabled large-field automotive laser shadowgraphy and was found to be very useful in similar practice situations, where a laser beam of high divergence is required. The diffuser setup was tested successfully in composing laser shadowgraph of 2 m height and 18 m width for a test car interior, where both direct and indirect driver’s fields of views are presented.
This paper studied the effect of replacing automotive reflection mirrors with digital mirrors (DM) on the driver’s safety and comfort. In this perspective, a new classification for DMs was proposed ...in appendance with a brief overview of driver’s visual ergonomics, associated with comparison and criticism of automotive DM design causality. Automotive laser shadowgraphy from car interior was performed to measure the obscuration in the driver’s direct field of view, due to the presence of DM displays at nine different obscuration positions. The driver’s off-road glances time toward DM displays was measured using a digital video recorder system. The eye glances of thirty drivers of different ages during different traffic conditions and their opinion about each display position through a simple questionnaire were statistically analyzed. Some pilot solutions were also proposed to reduce the expected drawbacks of such technology.
Proper mission control plays a key role in the lifetime of space mission operation, as it ensures that all resources are efficiently utilized when achieving mission goals. Ground control station ...operation mainly depends on received telemetry together with models simulating spacecraft`s subsystems. Created models help in raising the level of autonomy of MCC (Mission Control Center). Data driven models describe the actual state of the subsystem in real operation situations rather than theoretical costly physical models. This paper proposes data driven models for satellite battery subsystem based on Bayesian ridge regression algorithm. The ridge coefficients minimize a penalized residual sum of squares Thirty models of all thirty battery variables (capacitance, voltage, pressure and temperature) are built from normal operation data. Sensor reading value can be predicted from an observation of all other 29 values. Faults present in sensors or in system can be detected if predicted values are not equal to actual downloaded data from satellite. Bayesian ridge regression models are validated in terms of slope, intercept, R2-value, Q2 -value P-value and standard error.
Recently, Mission safety become an important concern because of the exponentially increment of space objects crossing or accompany the orbit. In such a situation the risk value becomes more and more ...as these controlled and uncontrolled objects increase. Therefore, mission control centers depend on organizations as joint space operation center to use their supplied information to schedule a smart plan to minimize the orbit risk. This paper proposes a new technique in the field of satellite safe trajectory incorporating orbital separation distance between different orbiting objects in artificial potential field method rather than position difference as in many cases the difference between them is enormous. Satellite surroundings are represented by artificial field where counterpart objects are represented by repulsive potentials, and future predicted path as an attractive field. Therefore, prospective planned maneuver considers all surrounding objects with different probability within the same algorithm. The proposed method is then applied to a real case between a Chinese "cz_4" and the United States "DMSP 5D-2 F7" satellites and show the results before and after applying the algorithm with calculations of the velocity required to escape risk situation and maintain the necessary orbital parameters. Finally, a comparative study is implemented to determine the effectiveness of the proposed method compared to the well-known Hohmann maneuver.
Control ground station performs satellite commanding and onboard subsystems health monitoring in addition to satellite tracking (orbit determination) and attitude determination based on received ...telemetry. The traditional approach for satellite health monitoring is to check sensor values whether being within permissible ranges or not. This paper proposes a supervised Naïve Bayesian classifier to build data driven models that detect power supply system anomaly. Battery flight test data has been used as the abnormal class, representing possible failures, an approach to overcome the problem of unavailability of labeled abnormal data in a supervised classification. Data used to build the model is a three months observation of battery's capacitance, voltage, temperature and pressure. Data has been subjected to principal component analysis before Naïve Bayesian classifier model building for visualization, labeling training data and to increase the variables independence as a restriction imposed by the naïve Bayesian classifier. The built Naïve Bayesian classifier model is validated using real faulty data in terms of accuracy, recall, precision, Fl-score and ROC curve.
This paper investigates the trajectory tracking control of an autonomous tracked vehicle. First, the desired linear and angular velocities are evaluated based on vehicle’s kinematics. An optimized ...backstepping controller is proposed as the kinematic controller, whereas the controller gains are optimally obtained. Next, an integral sliding mode control (SMC) is exploited based on vehicle dynamics and slipping characteristics, to obtain the desired torques that drive the vehicle and converge its trajectory to the desired one. Moreover, stability analysis of the whole system is proven based on Lyapunov theory. Finally, simulations and real-time experiments based on robot operating system (ROS) implementation are conducted to validate the effectiveness of the proposed control algorithm and compared with a hybrid backstepping-modified PID dynamic controller.
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