ИССЛЕДОВАНИЯ ПОВОРАЧИВАЕМОСТИ ТРАКТОРА ДЛЯ ПОСТРОЕНИЯ ГИБРИДНОЙ ТЕОРИИ ПОВОРОТА

Turn is one of the main and functionally difficult types of the movement of the wheel mobile traction and transportation vehicles (TTV): cars, tractors, self-propelled chassis, all-terrain vehicles. Settlement and experimental questions of kinematics of TTV turn are considered, settlement schemes of turn of all-wheel drive running system are chosen, models of kinematic wheel slip are created. Turn of a tractor without and with traction loading at the switched-off and switched on gear sistem of driving axle is experimentally investigated. Results of settlement pilot studies and the received mathematical models of turn of the machine make it possible to increase the accuracy of the set trajectory of the movement and to make adjustments of the machine and tractor unit movement, to reduce turn radius. At circular turn with traction load of 12 kN speeds center moving back behind driving axle of a tractor with the interaxal blocked drive equals 2.9 m, and coefficient of a kinematic discrepancy is 1.05 while with the interaxal differentiated drive these sizes are equal 1.03 and 0.93 respectively. Thus radiuses of turn of tractors with various drives differ insignificantly: 6.5 and 6.7 respectively. It is noted that at TTV on shell tires without traction loading the longitudinal coordinate of the speeds center is displaced from an axis of wheels rotation by 0.15 m back, side slip angle of forward wheels moving equals plus of 5.0 and plus of 7.8 degrees, kinematic wheel slips come nearer to 0. At circular turn with traction loading the speeds center moving back behind an axis of the back driving axle makes minus of 1.8 m and minus of 0.9 m, angles of kinematic moving within 5-10 degrees; angles of side moving are equal minus 5 and minus 7 degrees.Одним из основных и функционально сложных видов движения колесных мобильных тягово-транспортных средств (ТТС) - автомобилей, тракторов, самоходных шасси, вездеходов - является поворот. В данной работе рассмотрены расчетно-экспериментальные вопросы кинематики поворота ТТС, выбраны расчетные схемы поворота полноприводной ходовой системы, сформированы модели кинематического увода колес. Экспериментально исследованы поворот трактора без и с тяговой нагрузкой при отключенном и включенном приводе ведущего моста. Результаты расчетно-экспериментальных исследований и полученные математические модели поворота машины позволяют повысить точность заданной траектории и ввести коррективы в движение машинно-тракторного агрегата, снизить радиус поворота. Показали, что при круговом повороте с тяговой нагрузкой 12 кН смещение центра скоростей назад за задний ведущий мост у трактора с межосевым блокированным приводом равно 2,9 м и коэффициент кинематического несответствия - 1,05, в то время как с межосевым диффиренцированным приводом эти величины равны 1,03 и 0,93 соответственно. При этом радиусы поворота у тракторов с различными приводами отличаются несущественно - 6,5 и 6,7 соответственно. Отмечено что у ТТС на оболочковых шинах без тяговой нагрузки продольная координата центра скоростей смещена назад от оси вращения колес на 0,15 м, передние колеса перемещаются под углами бокового увода 5,0 и 7,8 градуса, углы кинематического увода малы и приближаются к 0. При круговом повороте с тяговой нагрузкой смещение центра скорости назад за ось заднего ведущего моста составляет 1,8 и 0,9 м, углы кинематического увода находятся в пределах 5-10 градусов; уголы бокового увода - минус 5 и минус 7 градусов

MOGA 4WD: multi-objective genetic algorithm for four-wheel drive electrical vehicle torque distribution in challenging conditions: MOGA 4WD: algoritmo genético multi-objetivo para distribuição de torque em veículos elétricos com tração em quatro rodas e em condições desafiadoras

This article aims to present a strategy for multi-objective optimization based on torque distribution for electrical 4WD (four-wheel drive) vehicles. By considering applications on uneven terrain, common to the navigation of tractors, off-road vehicles, or even mobile robots, an algorithm is developed having as input the vehicle attitude and output the controlled torque on each actuated wheel. The main criterion adopted is to guarantee the execution of a stable trajectory. And, to avoid wheel slippage, which occurs when low torques are applied, as well as vehicle rollover, which can occur in the presence of high torques, it is necessary to use two objective functions. To find the Pareto optimal solutions, the simplified dynamic model of a vehicle is adopted, considering a quasi-static motion. For each vehicle, its electrical, mechanical, and geometric characteristics can be used as formulation constraints. From an optimization performed offline, and adopting a polynomial approximation-based approach for real-time application, simulations and experiments show an interesting behavior: solutions that go beyond allowing the ascent of simple ramps or the overcoming of smooth obstacles are found - it is possible, for example, to climb ramps with high slopes, taking the vehicle to the limit between stability and instability

A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification

This paper introduces a comparison of training algorithms of radial basis function (RBF) neural networks for classification purposes. RBF networks provide effective solutions in many science and engineering fields. They are especially popular in the pattern classification and signal processing areas. Several algorithms have been proposed for training RBF networks. The Artificial Bee Colony (ABC) algorithm is a new, very simple and robust population based optimization algorithm that is inspired by the intelligent behavior of honey bee swarms. The training performance of the ABC algorithm is compared with the Genetic algorithm, Kalman filtering algorithm and gradient descent algorithm. In the experiments, not only well known classification problems from the UCI repository such as the Iris, Wine and Glass datasets have been used, but also an experimental setup is designed and inertial sensor based terrain classification for autonomous ground vehicles was also achieved. Experimental results show that the use of the ABC algorithm results in better learning than those of others

A New Terrain Classification Framework Using Proprioceptive Sensors for Mobile Robots

Mobile robots that operate in real-world environments interact with the surroundings to generate complex acoustics and vibration signals, which carry rich information about the terrain. This paper presents a new terrain classification framework that utilizes both acoustics and vibration signals resulting from the robot-terrain interaction. As an alternative to handcrafted domain-specific feature extraction, a two-stage feature selection method combining ReliefF and mRMR algorithms was developed to select optimal feature subsets that carry more discriminative information. As different data sources can provide complementary information, a multiclassifier combination method was proposed by considering a priori knowledge and fusing predictions from five data sources: one acoustic data source and four vibration data sources. In this study, four conceptually different classifiers were employed to perform the classification, each with a different number of optimal features. Signals were collected using a tracked robot moving at three different speeds on six different terrains. The new framework successfully improved classification performance of different classifiers using the newly developed optimal feature subsets. The greater improvement was observed for robot traversing at lower speeds

Rigidity-Based Surface Recognition for a Domestic Legged Robot

Although the infrared (IR) range and motor force sensors have been rarely applied to the surface recognition of mobile robots, they are fused in this paper with accelerometer and ground contact force sensors to distinguish six indoor surface types. Their sensor values are affected by the crawling gait period, therefore, certain components of the fast Fourier transform over these data are included in the feature vectors as well as remarkable discriminative power is observed for the same scalar statistics of different sensing modalities. The machine learning aspects are analyzed with random forests (RF) because of their stable performance and some inherent, beneficial properties for the model development process. The robustness is evaluated with unseen data after the model accuracy is estimated with cross-validation (CV), and regardless whether a Sony ERS-7 walks barefoot or wears socks, the forests achieve 94% accuracy. This result outperforms the state of the art techniques for indoor surfaces in the literature and the classification execution is real-time on the robot. The above mentioned model development process with RF is documented to create new models for other robots more quickly and efficiently

Arquitetura de hardware dedicada de uma rede neural perceptron para reconhecimento de terreno aplicado a robótica móvel

Monografia (graduação)—Universidade de Brasília, Faculdade UnB Gama, Curso de Engenharia Eletrônica, 2014.Este trabalho visa à implementação em FPGAs (Field Programmable Gate Arrays) de uma rede neural perceptron multicamadas para a classificação de terreno. Para tal, utilizou-se um acelerômetro de 3 eixos para medir as variações de aceleração que um robô sofre em quatro tipos de terreno: arenoso, asfalto, grama e terra. Uma rede neural do tipo perceptron multicamada foi treinada para poder realizar o processo de classificação. Após o treinamento da rede, obteve-se os pesos e os bias da rede para realizar a descrição em hardware e implementação do modelo matemático da rede em FPGAs. Resultados experimentais demonstraram que o desempenho em termos do erro de classificação é melhorado quando os dados de entrada são uma medida estatística da aceleração. Foi usada a média de 32 amostras para compor cada conjunto de entrada da rede. Adicionalmente, os resultados demonstraram que a rede treinada com 8 neurônios na camada escondida alcança o melhor fator custo-benefício. Comparações numéricas entre os resultados obtidos em software e hardware foram realizados para validação da arquitetura, demonstrando a corretude da implementação. Finalmente, os circuitos desenvolvidos foram caracterizados em termos do consumo de recursos, frequência de operação e consumo de potência. O tempo de execução em diversas plataformas embarcadas foi estimado, demonstrando que a arquitetura proposta alcança fatores de aceleração de três ordens de magnitude se comparado com processadores de software embarcados MicroBlaze e Atmel, e quatro vezes se comparado com um processador Intel Core i7. _____________________________________________________________________________ ABSTRACTThis work proposes an FPGA (Field Programmable Gate Arrays) implementation of a multilayer perceptron neural network for terrain classification. A 3-axis accelerometer was used for acquiring the acceleration variation that a robot suffers when moving on four different terrains: sand, asfalt, grass and soil. A multilayer perceptron neural network was trained in order to perform the classification process. Afterwards, the trained weight and bias were used to implement in FPGAs a hardware mathematical model of the proposed network. Experimental results have demonstrated that the network performance in terms of classification error was improved when using statistical values of the acceleration as input data. Thus, the mean value of 32 samples was computed in order to compose the input data set of the proposed neural netwrok. Numerical comparisons between hardware and software results, using the Matlab as statistical estimator, were used for validating the hardware implementation. Finally, the implemented circuits were characterizaed in terms of the consumption of hardware resources, operational frequency and power consumption. The execution time using three software-based embedded platforms were estimated. The proposed architecture achieves speed-up factors of three order of magnitude in comparison with the MicroBlaze and Atmel software processors, as well as, four times in comparision with an Intel Core i7 solution

Исследование эффектов проскальзывания при навигации мобильного робота при движении по неоднородным поверхностям

В работе представлено исследование проскальзывания колёс мобильного робота в задаче навигации при движении по неоднородной поверхности. Были проведены эксперименты по получению данных о движении робота по поверхностям с различным свойствами. На основе экспериментальных данных были построены модели зависимости коэффициента проскальзывания колеса от тока двигателя с учётом нормированной угловой скорости колёс. Для фильтрации шумов в измерениях тока двигателей был настроен фильтр Калмана. В заключении была проведена апробация работы системы оценки проскальзывания колёс мобильного робота.This paper presents a study of the wheel slippage of a mobile robot in a navigation problem while moving on a heterogeneous surface. Experiments were carried out to obtain data on the motion of the robot on surfaces with different properties. Models of the relationship between wheel slippage and motor current, taking into account the normalized angular velocity of the wheels, were constructed based on the experimental data. A Kalman filter was tuned to filter the noise in the motor current measurements. Finally, the wheel slippage estimation system of the mobile robot was tested