To accomplish reliable pedestrian detection using unmanned aerial vehicles (UAVs) under night-time conditions, an image enhancement method is developed in this article to improve the low-illumination image quality. First, the image brightness is mapped to a desirable level by a hyperbolic tangent curve. Second, the block-matching and 3-D filtering methods are developed for an unsharp filter in YCbCr color space for image denoising and sharpening. Finally, pedestrian detection is performed using a convolutional neural network model to complete the surveillance task. Experimental results show that the Minkowski distance measurement index of enhanced images is increased to 0.975, and the detection accuracies, in F-measure and confidence coefficient, reach 0.907 and 0.840, respectively, which are the highest as compared with other image enhancement methods. This developed method has potential values for night-time UAV visual monitoring in smart city applications.

Autonomous guidance of agricultural vehicles is vital as mechanized farming production becomes more prevalent. It is crucial that tractor-trailers are guided with accuracy in both lateral and longitudinal directions, whilst being affected by large disturbance forces, or slips, owing to uncertain and undulating terrain. Previous research has been concentrated on trajectory control that provides longitudinal and lateral accuracy if the vehicle moves without sliding and/or the trailer is passive. In this paper, we extend current results by addressing the problem of robust trajectory tracking along straight and circular paths of a tractors with steerable trailers. We develop a controller that is a robust combination of a backstepping and nonlinear PI control. For vehicles subjected to slip, the proposed controller makes the lateral deviations and the orientation errors of the tractor and trailer converge to a neighborhood near the origin. Simulation results are presented to illustrate that the suggested controller ensures precise trajectory tracking in the presence of slip.

Categorization based on objects is an effective way to integrate spectral and spatial information into remote sensing image classification. In this paper, we establish a classification framework which represents objects by super pixels. The non-parametric k-NN approach is chosen for this super pixel based method, as it is simple and free of class data distribution. A new descriptor for the features distribution of each super pixel, called 4-D color histograms, is used for both spectral and texture information. This descriptor provides a better tolerance for value fluctuations inside the super pixel. Furthermore, the Ç2distance is used as the measure of the similarity between color histograms of the super pixels. Experiments are conducted to illustrate the application of the proposed method.

The contrast enhancement of gray-level digital images is considered in this paper. In particular, the mean image intensity is preserved while the contrast is enhanced. This provides better viewing consistence and effectiveness. The contrast enhancement is achieved by maximizing the information content carried in the image via a continuous intensity transform function. The preservation of image intensity is obtained by applying gamma-correction on the images. Since there is always a trade-off between the requirements for the enhancement of contrast and preservation of intensity, an improved multiobjective particle swarm optimization procedure is proposed to resolve this contradiction, making use of its flexible algorithmic structure. The effectiveness of the proposed approach is illustrated by a number of images including the benchmarks and an image sequence captured from a mobile robot in an indoor environment.

This paper presents the design of a new hybrid filter for quality improvement of power systems in unbalanced conditions. This filter includes a variable passive filter in parallel with an active filter. The passive part is designed with a varying inductance to reduce the rating of the active part. By using this proposed filter it is possible to compensate for the current harmonics and unbalanced loads with a lower rating active filter compared with conventional ones. Also a simple and accurate method is used to generate the harmonic reference current in the active part of this filter which makes it efficient in unbalanced load conditions. Furthermore, an intelligent hysteresis band current controller is utilized to generate inverter pulses owing to its quick current controllability and easy implementation. Simulation results using the MATLAB Simulink Power System Toolbox show the efficiency and effectiveness of the proposed hybrid filter in elimination of harmonics and balance of the utility current with a desired level of load balancing.

Evolutionary computation algorithms, such as the particle swarm optimization (PSO), have been widely applied in numerical optimizations and real-world product design, not only for their satisfactory performances but also in their relaxing the need for detailed mathematical modelling of complex systems. However, as iterative heuristic searching methods, they often suffer from difficulties in obtaining high quality solutions in an efficient manner. Since unnecessary resources used in computation iterations should be avoided, the determination of a proper termination condition for the algorithms is desirable. In this work, termination is cast as a decision-making process to end the algorithm. Specifically, the non-parametric sign- test is incorporated as a hypothetical test method such that a quantifiable termination in regard to specifiable decision-errors can be assured. Benchmark optimization problems are tackled using the PSO as an illustrative optimizer to demonstrate the effectiveness of the proposed termination condition.

When using an extended Kalman filter (EKF) in simultaneous localization and mapping (SLAM) for a mobile robot with bearing-only measurements, it is crucial to correctly assign correspondences between measurements and registered features in the map, otherwise the filter diverges or becomes inconsistent. Conventional methods based on the Mahalanobis distance metric may produce data association ambiguities. Its reliability may further be degraded in bearing-only SLAM due to the limited amount of information delivered from the sensor. The data association process is cast here as that of making a decision based on the sensor measurement as whether to update the EKF or not. For this, cost functions are applied taking into account the interferences from other features. The proposed approach enhances robustness of the data association and consequently assures the performance of bearing-only SLAM. Results from simulations and experiments are included to demonstrate the effectiveness of the method in a typical indoor scenario.

The F2(Force Field) method is a novel approach for multi-robot motion planning and collision avoidance. The setting of parameters is however vital to its performance. This paper presents an approach using Particle Swarm Optimization (PSO) to properly determine the control parameters for the F2method. The goal of the optimization is to minimize the resultant path lengths. The approach presented in this paper can be used as a tool to obtain optimal parameters for various tasks before their execution. Simulations are carried out in various environments to show the feasibility of this approach.

The effects of randomness of control coefficients in particle swarm optimization (PSO) are investigated through empirical studies. The PSO is viewed as a method to solve a coverage problem in the solution space when the global-best particle is reported as the solution. Randomness of the control coefficients, therefore, plays a crucial role in providing an efficient and effective algorithm. Comparisons of performances are made between the uniform and Gaussian distributed random coefficients in adjusting particle velocities. Alternative strategies are also tested, they include: i) pre-assigned randomness through the iterations, ii) selective hybrid random adjustment based on the fitness of the particles. Furthermore, the effect of velocity momentum factor is compared between a constant and random momentum. Numerical results show that performances of the proposed variations are comparable to the conventional implementation for simple test functions. However, enhanced performances using the selective and hybrid strategy are observed for complicate functions.

This paper presents an effective methodology for the control of a group of mobile robots moving in desired formations. Virtual head robot tracking and three-point l-l controllers incorporated with reactive control schemes are proposed here to overcome the cases when the control law is undefined and subject to potential inter-robot collision. In order for the whole group to enter a desired formation, a general procedure is suggested in the initialisation stage. Advantages of our approach include the establishment and maintenance of any formation from arbitrary initial conditions with collision avoidance for a large group of mobile robots. The approach is validated through simulation cases to illustrate the capability of handling singularities and avoiding collision in a group of N robots

In this paper, the possibility and necessity of multi-step trajectory planning in Extended Kalman Filter (EKF) based SLAM is investigated. The objective of the trajectory planning here is to minimize the estimation error of the robot and landmark locations subject to a given time horizon. We show that the problem can be regarded as an optimization problem for a gradually identified model. A numerical method is proposed for trajectory planning using a variant of the nonlinear Model Predictive Control (MPC). The proposed method is optimal in the sense that the control action is computed using all the information available at the time of decision making. Simulation results are included to compare the results from the one-step look-ahead trajectory planning and the proposed multi-step look-ahead technique.

Use of a Gaussian Sum filter (GSF) to efficiently solve the initialisation problem in bearing-only simultaneous localisation and mapping (SLAM) is the main contribution of this paper. When information about the range is not available, the initial probability density function (pdf) of a landmark in the environment can not be represented using a Gaussian. The GSF is an attractive candidate for estimation in this scenario as it can deal with arbitrary pdfs represented as sets of Gaussians. However, the implementation of the GSF requires maintaining a bank of extended Kalman filters. The resulting computational complexity needs to be reduced by employing a minimum number of filters. In this work, the performance of each extended Kalman filter (EKF) in the GSF is evaluated using the sequential probability ratio test (SPRT). As such the number of members in the Gaussian sum can be reduced rapidly and the efficiency of the GSF can be significantly increased, providing a solution to the important problem of bearing-only SLAM. The effectiveness of the proposed approach is demonstrated by simulation and experiment conducted using a Pioneer mobile robot.

The main contribution of this paper is an algorithm for integrating motion planning and simultaneous localisation and mapping (SLAM). Accuracy of the maps and the robot locations computed using SLAM is strongly dependent on the characteristics of the environment, for example feature density, as well as the speed and direction of motion of the robot. Appropriate control of the robot motion is particularly important in bearing-only SLAM, where the information from a moving sensor is essential. In this paper a near minimum time path planning algorithm with a finite planning horizon is proposed for bearing-only SLAM. The objective of the algorithm is to achieve a predefined mapping precision while maintaining acceptable vehicle location uncertainty in the minimum time. Simulation results have shown the effectiveness of the proposed method.

The sample impoverishment problem in particle filters is investigated from the perspective of genetic algorithms. The contribution of this paper is in the proposal of a hybrid technique to mitigate sample impoverishment such that the number of particles required and hence the computation complexities are reduced. Studies are conducted through the use of Chebyshev inequality for the number of particles required. The relationship between the number of particles and the time for impoverishment is examined by considering the takeover phenomena as found in genetic algorithms. It is revealed that the sample impoverishment problem is caused by the resampling scheme in implementing the particle filter with a finite number of particles. The use of uniform or roulette-wheel sampling also contributes to the problem. Crossover operators from genetic algorithms are adopted to tackle the finite particle problem by re-defining or re-supplying impoverished particles during filter iterations. Effectiveness of the proposed approach is demonstrated by simulations for a monobot simultaneous localization and mapping application.

This paper presents a multiple hypothesis approach to solve the simultaneous localisation and mapping (SLAM) problem with a bearing-only sensor. The main contribution of the paper is to provide a remedy for the landmark initialisation problem that occurs due to the absence of range information, in a computationally efficient manner. Each landmark is initialised in the form of multiple hypotheses distributed along the direction of the bearing measurement. Using subsequent measurements, the validity of the hypotheses is evaluated based on the sequential probability ratio test (SPRT). Consequently, the best approximation to the landmark location is maintained. This approach enables an extended Kalman filler (EKF) to be used for bearing-only SLAM providing a computational efficient solution. Simulation and experimental results, from using a camera as the bearing-only sensor mounted on a Pioneer robot are included to demonstrate the effectiveness of the proposed technique.