Manufacturing systems of the future foresee the use of intelligent vehicles, optimizing and navigating. The navigational problem is an important and challenging problem in the field of robotics. The robots often find themselves in a situation where they must find a trajectory to another position in their environment, subject to constraints posed by obstacles and the capabilities of the robot itself. On-line navigation is a set of algorithms that plans and executes a trajectory at the same time. The system adopted in this research searches for a robot collision-free trajectory in a dynamic environment in which obstacles can move while the robot was moving toward the target. So, the robot must operate in real-time such that the system reacts to unexpected obstacles. Genetic algorithms that have been used successfully in many search problems are used to solve the on-line navigation problem with less computational cost. The system uses genetic algorithm as a search method for an optimal trajectory.
This article proposes a new strategy based on a hybrid method that combines the gravitational search algorithm (GSA) with the bat algorithm (BAT) to solve a single-objective optimization problem. It first runs GSA, followed by BAT as the second step. The proposed approach relies on a parameter between 0 and 1 to address the problem of falling into local research because the lack of a local search mechanism increases intensity search, whereas diversity remains high and easily falls into the local optimum. The improvement is equivalent to the speed of the original BAT. Access speed is increased for the best solution. All solutions in the population are updated before the end of the operation of the proposed algorithm. The diversification f
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Intended for getting good estimates with more accurate results, we must choose the appropriate method of estimation. Most of the equations in classical methods are linear equations and finding analytical solutions to such equations is very difficult. Some estimators are inefficient because of problems in solving these equations. In this paper, we will estimate the survival function of censored data by using one of the most important artificial intelligence algorithms that is called the genetic algorithm to get optimal estimates for parameters Weibull distribution with two parameters. This leads to optimal estimates of the survival function. The genetic algorithm is employed in the method of moment, the least squares method and the weighted
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In this research, the focus was placed on estimating the parameters of the Hypoexponential distribution function using the maximum likelihood method and genetic algorithm. More than one standard, including MSE, has been adopted for comparison by Using the simulation method
An indoor spraying robot is built in this research to solve numerous challenges associated with manual spraying. The mechanical, hardware and essential technologies used are all detailed and designed. The proposed spraying robot's conceptual design is split into two parts: hardware and software. The mechanical design, manufacturing, electrical, and electronics systems are described in the hardware part, while the control of the robot is described in the software section. This robot's kinematic and dynamic models were developed using three links that move in the x, y, and z directions. The robot was then designed using SolidWorks software to compute each connection's deflection and maximum stresses. The characteristics of the stepper moto
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When soft tissue planning is important, usually, the Magnetic Resonance Imaging (MRI) is a medical imaging technique of selection. In this work, we show a modern method for automated diagnosis depending on a magnetic resonance images classification of the MRI. The presented technique has two main stages; features extraction and classification. We obtained the features corresponding to MRI images implementing Discrete Wavelet Transformation (DWT), inverse and forward, and textural properties, like rotation invariant texture features based on Gabor filtering, and evaluate the meaning of every
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A system was used to detect injuries in plant leaves by combining machine learning and the principles of image processing. A small agricultural robot was implemented for fine spraying by identifying infected leaves using image processing technology with four different forward speeds (35, 46, 63 and 80 cm/s). The results revealed that increasing the speed of the agricultural robot led to a decrease in the mount of supplements spraying and a detection percentage of infected plants. They also revealed a decrease in the percentage of supplements spraying by 46.89, 52.94, 63.07 and 76% with different forward speeds compared to the traditional method.
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