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.

This paper presents a survey on existing stereo vision algorithms. The existing stereo vision algorithms are discussed in terms of concept, performance and related improvements. Also, a brief analysis of performance comparison among existing stereo vision algorithms is presented. Moreover, available improvements and solutions for stereo vision challenges such as computational complexity, occlusion, radiometric distortion, depth discontinuity and textureless region are reviewed.

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.

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.

Genetic algorithm has been widely used in numerous fields. Recently, researchers have developed significant interest in applying GA in unmanned aerial vehicle path planning. However, experiments should be conducted to determine selection schemes and population sizes. This process is time consuming and inconvenient for users. Thus, a study is conducted on the performance of GA at various selection schemes and population sizes. Results show that large population sizes do not contribute in improving the performance of GA. Tournament selection shows the highest performance in terms of path cost, whereas the weakest performance is in terms of computational cost. Truncation selection shows the optimum performance among the selection schemes.