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Genetic algorithm has been widely used in numerous fields, including in solving structural optimization problems, financial decision problems, and docking of flexible ligands, among others. Recently, researchers have developed significant interest in applying genetic algorithm in unmanned aerial vehicle path planning. Despite the advantages of genetic algorithm, several parameter inputs are required before using this algorithm. They include waypoint, population size, crossover rate, and mutation rate. The performance of this algorithm is dependent on these parameter settings. Thus, parameter optimization is performed on this algorithm to skip the tuning parameter process during unmanned aerial vehicle path planning. Results show that truncation selection at 20% is highly recommended for genetic algorithm path planning application because of its low average path and computational costs. In addition, a population size of 40 is suitable in unmanned aerial vehicle path planning. Last but not least, the optimum mutation rate of 10% is recommended based on the average path and computational cost for this algorithm. [ABSTRACT FROM AUTHOR]

The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost. [ABSTRACT FROM AUTHOR]

This study investigates the longitudinal motion control of unmanned aerial vehicles through a simulation in MATLAB. The linear model of an unmanned aerial vehicle is applied to controllers to explicate the longitudinal motion of the unmanned aerial vehicle. The ideal performance for an unmanned aerial vehicle is to achieve the desired response instantly with 100% precision. However, currently available controllers need further improvements. Thus, a proportional-derivative linear quadratic regulator controller is developed and compared with a proportional-integral-derivative controller, a linear quadratic regulator controller, and a proportional linear quadratic regulator controller. The proportional-derivative linear quadratic regulator controller enhances the response of the system by reducing settling time by more than 95% compared with other available controllers. Additionally, the proportional-derivative linear quadratic regulator improves the root mean square error by almost 50% compared with the proportional linear quadratic regulator controller and improves rise time by almost 96% with reasonable overshoot. [ABSTRACT FROM AUTHOR]

This paper describes studies the longitudinal motion control of unmanned aerial vehicles (UAVs) by using a proportional-integral-derivative (PID) controller, linear-quadratic-regulator (LQR) controller, and P-LQR controller. The non-linear model of the motion is changed into a linear model and is implemented in the controllers to elucidate the longitudinal motion of UAVs. The control performance of the aircraft improves when the UAV reaches the desired response instantaneously and precisely. However, the performance of currently available controllers is not ideal, and a 10% improvement in controller performance can lead to a significant effect. Thus, the P-LQR controller is developed. This controller significantly improves the response times of the control system by at least 38.55% compared with other existing controllers. Hence, an alternative longitudinal motion control for UAVs has been proposed. [ABSTRACT FROM AUTHOR]

Despite years of work, a robust, widely applicable generic "symmetry detector" that can paral-lel other kinds of computer vision/image processing tools for the more basic structural charac-teristics, such as a "edge" or "corner" detector, remains a computational challenge. A new symmetry feature detector with a descriptor is proposed in this paper, namely the Simple Robust Features (SRF) algorithm. A performance comparison is made among SRF with SRF, Speeded-up Robust Features (SURF) with SURF, Maximally Stable Extremal Regions (MSER) with SURF, Harris with Fast Retina Keypoint (FREAK), Minimum Eigenvalue with FREAK, Features from Accelerated Segment Test (FAST) with FREAK, and Binary Robust Invariant Scalable Keypoints (BRISK) with FREAK. A visual tracking dataset is used in this performance evaluation in terms of accuracy and computational cost. The results have shown that combining the SRF detector with the SRF descriptor is preferable, as it has on average the highest accuracy. Additionally, the computational cost of SRF with SRF is much lower than the others. [ABSTRACT FROM AUTHOR]