Search results

The purpose of this study is to solve the problems of poor stability and high energy consumption of the dynamic window algorithm (DWA) for the mobile robots, a novel enhanced dynamic window algorithm is proposed in this paper.

The novel algorithm takes the distance function as the weight of the target-oriented coefficient, and a new evaluation function is presented to optimize the azimuth angle.

The jitter of the mobile robot caused by the drastic change of angular velocity is reduced when the robot is closer to the target point. The simulation results show that the proposed algorithm effectively optimizes the stability of the mobile robot during operation with lower angular velocity dispersion and less energy consumption, but with a slightly higher running time than DWA.

A novel enhanced dynamic window algorithm is proposed and verified. According to the experimental result, the proposed algorithm can reduce the energy consumption of the robot and improves the efficiency of the robot.

The purpose of this paper is to propose a whole set of methods for underwater target detection, because most underwater objects have small samples, low quality underwater images problems such as detail loss, low contrast and color distortion, and verify the feasibility of the proposed methods through experiments.

The improved RGHS algorithm to enhance the original underwater target image is proposed, and then the YOLOv4 deep learning network for underwater small sample targets detection is improved based on the combination of traditional data expansion method and Mosaic algorithm, expanding the feature extraction capability with SPP (Spatial Pyramid Pooling) module after each feature extraction layer to extract richer feature information.

The experimental results, using the official dataset, reveal a 3.5% increase in average detection accuracy for three types of underwater biological targets compared to the traditional YOLOv4 algorithm. In underwater robot application testing, the proposed method achieves an impressive 94.73% average detection accuracy for the three types of underwater biological targets.

Underwater target detection is an important task for underwater robot application. However, most underwater targets have the characteristics of small samples, and the detection of small sample targets is a comprehensive problem because it is affected by the quality of underwater images. This paper provides a whole set of methods to solve the problems, which is of great significance to the application of underwater robot.

Bayesian cubature (BC) has emerged to be one of most competitive approach for estimating the multi-dimensional integral especially when the integrand is expensive to evaluate, and alternative acquisition functions, such as the Posterior Variance Contribution (PVC) function, have been developed for adaptive experiment design of the integration points. However, those sequential design strategies also prevent BC from being implemented in a parallel scheme. Therefore, this paper aims at developing a parallelized adaptive BC method to further improve the computational efficiency.

By theoretically examining the multimodal behavior of the PVC function, it is concluded that the multiple local maxima all have important contribution to the integration accuracy as can be selected as design points, providing a practical way for parallelization of the adaptive BC. Inspired by the above finding, four multimodal optimization algorithms, including one newly developed in this work, are then introduced for finding multiple local maxima of the PVC function in one run, and further for parallel implementation of the adaptive BC.

The superiority of the parallel schemes and the performance of the four multimodal optimization algorithms are then demonstrated and compared with the k-means clustering method by using two numerical benchmarks and two engineering examples.

Multimodal behavior of acquisition function for BC is comprehensively investigated. All the local maxima of the acquisition function contribute to adaptive BC accuracy. Parallelization of adaptive BC is realized with four multimodal optimization methods.

Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).

The significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.

The efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.

This paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.

The motion vector estimation algorithm is very widely used in many image process applications, such as the image stabilization and object tracking algorithms. The conventional searching algorithm, based on the block matching manipulation, is used to estimate the motion vectors in conventional image processing algorithms. During the block matching manipulation, the violent motion will result in greater amount of computation. However, too large amount of calculation will reduce the effectiveness of the motion vector estimation algorithm. This paper aims to present a novel searching method to estimate the motion vectors effectively.

This paper presents a novel searching method to estimate the motion vectors for high-resolution image sequences. The searching strategy of this algorithm includes three steps: the larger area searching, the adaptive directional searching and the small area searching.

The achievement of this paper is to develop a motion vector searching strategy to improve the computation efficiency. Compared with the conventional motion vector searching algorithms, the novel motion vector searching algorithm can reduce the motion matching manipulation effectively by 50 per cent.

This paper presents a novel searching strategy to estimate the motion vectors effectively. From the experimental results, the novel motion vector searching algorithm can reduce the motion matching manipulation effectively, compared with the conventional motion vector searching algorithms.

A new algorithm for reducing the profile and root‐mean‐square wavefront of sparse matrices with a symmetric structure is presented. Our numerical experiments show an overall better performance than the widely used reverse Cuthill‐McKee, Gibbs‐King and Sloan algorithms. The new algorithm is fast, simple and useful in engineering analysis where it can be employed to derive efficient orderings for both profile and frontal solution schemes.

The purpose of this paper is to provide a minimum time algorithm to intercept an object on a conveyor belt by a robotic manipulator. The goal is that the robot is able to intercept objects on a conveyor line moving at a given speed in minimum time.

In order to formulate the problem, the robot and object‐arrival time functions were introduced, and conclude that the optimal point occurs at the intersection of these two functions. The search algorithm for finding the intersection point between the robot and object arrival time functions are also presented to find the optimal point in real‐time.

Simulation results show that the presented algorithm is well established for various initial robot positions.

A trapezoidal velocity profile was employed which is used in many industrial robots currently in use. Thus, it is believed that robot travel time algorithm is readily implemented for any commercially available robots.

The paper considers exhaustive cases where robot travel time functions are dependent upon initial positions of robotic end‐effectors. Also presented is a fast converging search algorithm so that real time application is more feasible in many cases.

In stereolithgraphy rapid prototyping, the support can constrain the model deformation and avoid a lot of problems such as collapse, shift, and imbalance of the model. The support automatic generation algorithm has become the key research of rapid prototyping technics software; however, presently the efficiency of support automatic generation algorithm is low, and its algorithm is complex. Thus, it is of great importance in improving the overall efficiency of rapid prototyping technics software through enhancing the support generation efficiency. This paper aims to address these issues.

Based on this, this paper proposes a discrete‐marking support algorithm for treatment of processing on manufactured part model of all triangle‐based discrete‐marking on the support plane, which realizes the three‐dimensional computation of intersection for each line associated with the triangle automatically, and avoid invalid computation between all the support lines and other triangles.

The algorithm efficiency has reached O(n). Meanwhile, the technics support generation speed has been improved. Furthermore, the new mesh discrete‐marking and automatic support generation algorithm has been successfully applied to the sterolithography apparatus and selective laser melting of HRPS series.

Practical application indicates that the new support generation algorithm based on discrete‐marking considerably improves the support technics efficiency and stereolithgraphy rapid prototyping efficiency of the technics software.

The research presents a Noval support fast generation algorithm based on discrete‐marking in stereolithgraphy rapid prototyping.

The purpose of this paper is to use the proposed algorithm for the fast adaptation of the antenna array radiation pattern on the particular scenario of the incoming signals. The fitness function to be minimized includes the precise estimation of signals’ arrival angles, setting the deep nulls in the directions of the interfering signal, the reduction of the main lobe’s width and the reduction of side lobes.

Unlike conventional adaptive algorithms, the proposed algorithm allows synthesis of radiation patterns in the case of a larger number of incident desired and interfering signals. The proposed method also reduces the width of the dead zone.

In this paper a comparison of the results obtained from the chaotic beamforming algorithm with the results obtained by using the Sequential Quadratic Programming method is presented.

The chaotic beamforming algorithm is proposed here. It is based on the optimization of the least mean square and on the variable step-size least mean square algorithms, using chaos theory for synthesis of the radiation pattern of the linear antenna array.

This paper aims to develop and analyse a new multiple-hypothesis receiver autonomous integrity monitoring (RAIM) algorithm. The proposed algorithm can handle simultaneous multiple failures as well as a single failure.

The proposed algorithm uses measurement residuals and satellite observation matrices of several consecutive epochs for failure detection and exclusion. It detects failures by monitoring the error vector itself instead of monitoring the projection of the error vector. The algorithm reduces the minimum detectable bias via the relative receiver autonomous integrity monitoring (RRAIM) scheme.

The algorithm is able to detect any instance of multiple failures, including failures that are not detected by the conventional RAIM algorithm. It is able to detect multiple failures with magnitudes of several tens of meters, although the algorithm has to solve an ill-conditioning problem. The detection capability of the proposed algorithm is not dependent on satellite geometry.

The algorithm assumes that the error vectors in three consecutive epochs have biases of similar magnitude. As a result, although the algorithm detects occurrences of drifting error, it cannot identify which measurement(s) has the critical error.

The paper includes implications for the development of the RAIM algorithm for aviation users. Especially, it can be a candidate for future standard architecture in multiple constellations, multiple frequency satellite-based augmentation system (SBAS) users.

The paper proposes a new multiple-hypothesis RAIM algorithm with an RRAIM concept. A detailed explanation of the algorithms, including rigorous mathematical expressions, is presented. The paper also includes an analysis of differences in detection capability between conventional algorithm and the proposed algorithm depending on satellite geometry.