Increasing levels of autonomy impose more pronounced performance requirements for unmanned ground vehicles (UGV). Presence of model uncertainties significantly reduces a ground vehicle performance when the vehicle is traversing an unknown terrain or the vehicle inertial parameters vary due to a mission schedule or external disturbances. A comprehensive mathematical model of a skid steering tracked vehicle is presented in this paper and used to design a control law. Analysis of the controller under model uncertainties in inertial parameters and in the vehicle-terrain interaction revealed undesirable behavior, such as controller divergence and offset from the desired trajectory. A compound identification scheme utilizing an exponential forgetting recursive least square, generalized Newton–Raphson (NR), and Unscented Kalman Filter methods is proposed to estimate the model parameters, such as the vehicle mass and inertia, as well as parameters of the vehicle-terrain interaction, such as slip, resistance coefficients, cohesion, and shear deformation modulus on-line. The proposed identification scheme facilitates adaptive capability for the control system, improves tracking performance and contributes to an adaptive path and trajectory planning framework, which is essential for future autonomous ground vehicle missions.
This paper addresses the problem of using deep learning for makeup style transfer. For solving this problem, we propose a new supervised method. Additionally, we present a technique for creating a synthetic dataset for makeup transfer used to train our model. The obtained results were compared with six popular methods for makeup transfer using three metrics. The tests were carried out on four available data sets. The proposed method, in many respects, is competitive with the methods used in the literature. Thanks to images of faces with generated synthetic makeup, the proposed method learns to better transfer details, and the learning process is significantly accelerated.
In times of rapidly progressing globalization, the possibility of fast long-distance travel between high traffic cities has become an extremely important issue. Currently, available transportation systems have numerous limitations, therefore, the idea of a high-speed transportation system moving in reduced-pressure conditions has emerged recently. This paper presents an approach to the modelling and simulation of the dynamic behaviour of a simplified high-speed vehicle that hovers over the track as a magnetically levitated system. The developed model is used for control system design. The purpose of passive and active suspension discussed in the text is to improve both the performance and stability of the vehicle as well as ride comfort of passengers travelling in a compartment. Comparative numerical studies are performed and the results of the simulations are reported in the paper with the intent to demonstrate the benefits of the approach employed here.
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