An Improved VFF Approach for Robot Path Planning in Unknown and Dynamic Environments

Robot path planning in unknown and dynamic environments is one of the hot topics in the field of robot control. The virtual force field (VFF) is an efficient path planning method for robot. However, there are some shortcomings of the traditional VFF based methods, such as the local minimum problem and the higher computational complexity, in dealing with the dynamic obstacle avoidance. In this paper, an improved VFF approach is proposed for the real-time robot path planning, where the environment is unknown and changing. An area ratio parameter is introduced into the proposed VFF based approach, where the size of the robot and obstacles are considered. Furthermore, a fuzzy control module is added, to deal with the problem of obstacle avoidance in dynamic environments, by adjusting the rotation angle of the robot. Finally, some simulation experiments are carried out to validate and demonstrate the efficiency of the proposed approach

A Bioinspired Neural Model Based Extended Kalman Filter for Robot SLAM

Robot simultaneous localization and mapping (SLAM) problem is a very important and challenging issue in the robotic field. The main tasks of SLAM include how to reduce the localization error and the estimated error of the landmarks and improve the robustness and accuracy of the algorithms. The extended Kalman filter (EKF) based method is one of the most popular methods for SLAM. However, the accuracy of the EKF based SLAM algorithm will be reduced when the noise model is inaccurate. To solve this problem, a novel bioinspired neural model based SLAM approach is proposed in this paper. In the proposed approach, an adaptive EKF based SLAM structure is proposed, and a bioinspired neural model is used to adjust the weights of system noise and observation noise adaptively, which can guarantee the stability of the filter and the accuracy of the SLAM algorithm. The proposed approach can deal with the SLAM problem in various situations, for example, the noise is in abnormal conditions. Finally, some simulation experiments are carried out to validate and demonstrate the efficiency of the proposed approach

A Thermal Infrared and Visible Images Fusion Based Approach for Multitarget Detection under Complex Environment

Multitarget detection under complex environment is a challenging task, where the measured signal will be submerged by noise. D-S belief theory is an effective approach in dealing with Multitarget detection. However, there are some limitations of the general D-S belief theory under complex environment. For example, the basic belief assignment is difficult to establish, and the subjective factors will influence the update process of evidence. In this paper, a new Multitarget detection approach based on thermal infrared and visible images fusion is proposed. To easily characterize the defected heterogeneous image, a basic belief assignment based on the distance distribution function of heterogeneous characteristics is presented. Furthermore, to improve the discrimination and effectiveness of the Multitarget detection, a concept of comprehensive credibility is introduced into the proposed approach and a new update rule of evidence is designed. Finally, some experiments are carried out and the experimental results show the efficiency and effectiveness of the proposed approach in the Multitarget detection task

An Improved VFF Approach for Robot Path Planning in Unknown and Dynamic Environments

Robot path planning in unknown and dynamic environments is one of the hot topics in the field of robot control. The virtual force field (VFF) is an efficient path planning method for robot. However, there are some shortcomings of the traditional VFF based methods, such as the local minimum problem and the higher computational complexity, in dealing with the dynamic obstacle avoidance. In this paper, an improved VFF approach is proposed for the real-time robot path planning, where the environment is unknown and changing. An area ratio parameter is introduced into the proposed VFF based approach, where the size of the robot and obstacles are considered. Furthermore, a fuzzy control module is added, to deal with the problem of obstacle avoidance in dynamic environments, by adjusting the rotation angle of the robot. Finally, some simulation experiments are carried out to validate and demonstrate the efficiency of the proposed approach

A novel ML DOA estimation algorithm for array signal processing

A novel algorithm for finding the optimal solution of nonlinear function in maximum likelihood DOA estimation is proposed to reduce the calculation in multi-dimensional nonlinear search of the estimation. In the proposed method, firstly, the mode of population initialization mode is modified to improve the stability of population evolution. Secondly, the crossover operator is also improved to enlarge the range of new generated individual. Thirdly, parameters adaptive adjustment strategy is designed to accelerate convergence. The simulation shows that the proposed algorithm can greatly reduce the calculation time

A novel ML DOA estimation algorithm for array signal processing

A novel algorithm for finding the optimal solution of nonlinear function in maximum likelihood DOA estimation is proposed to reduce the calculation in multi-dimensional nonlinear search of the estimation. In the proposed method, firstly, the mode of population initialization mode is modified to improve the stability of population evolution. Secondly, the crossover operator is also improved to enlarge the range of new generated individual. Thirdly, parameters adaptive adjustment strategy is designed to accelerate convergence. The simulation shows that the proposed algorithm can greatly reduce the calculation time

Differences in prevalence of prehypertension and hypertension in children and adolescents in the eastern, central and western regions of China from 1991-2011 and the associated risk factors.

The present study aimed to estimate the differences in rates of prehypertension and hypertension in children and adolescents among three regions with different socioeconomic status in China, and explore the corresponding risk factors associated with prehypertension and hypertension to guide the prevention. Blood pressure measurements of 13 762 children and adolescents aged 6-17 years were obtained from a prospective national survey (the China Health and Nutrition Survey, 1991-2011). Prehypertension and hypertension were defined by age and gender, according to China's standard criteria. Chi-square tests were used to compare the differences in the prevalence of prehypertension and hypertension among three regions. Trend chi-square tests were used to detect the trends in rates of prehypertension and hypertension over survey years. Logistic regression models were used to detect the potential risk factors of prehypertension and hypertension in children and adolescents. During the survey years, the overall prevalence of prehypertension and hypertension were 6.0% and 10.6%. The corresponding rates in the western region were lowest, but increased rapidly over the two decades (84.0% and 122.6% increases respectively, P<0.001). The overall hypertension rate remained high in the eastern region, despite the slower increase (24.2% increase). In the central region, although the prehypertension rate remained stable, the rate of hypertension had a 94.8% increase these years (P<0.0001). According to the results of logistic regression, age, body mass index (BMI) and waist/height ratio (WHtR) were associated with prehypertension and hypertension. Children and adolescents in the eastern region had the highest level of prehypertension and hypertension, while the rapid increase of blood pressure in the western and central regions were also supposed to concern. Improvement of the healthy lifestyle is urgent for prehypertension and hypertension prevention in children and adolescents

A Single-Way Ranging Localization of AUVs Based on PSO of Outliers Elimination

Localization of autonomous underwater vehicles (AUVs) is a very important and challenging task for the AUVs applications. In long baseline underwater acoustic localization networks, the accuracy of single-way range measurements is the key factor for the precision of localization of AUVs, whether it is based on the way of time of arrival (TOA), time difference of arrival (TDOA), or angle of arrival (AOA). The single-way range measurements do not depend on water quality and can be taken from long distances; however, there are some limitations which exist in these measurements, such as the disturbance of the unknown current velocity and the outliers caused by sensors and errors of algorithm. To deal with these problems, an AUV self-localization algorithm based on particle swarm optimization (PSO) of outliers elimination is proposed, which improves the performance of angle of arrival (AOA) localization algorithm by taking account of effects of the current on the positioning accuracy and eliminating possible outliers during the localization process. Some simulation experiments are carried out to illustrate the performance of the proposed method compared with another localization algorithm

Bioinspired Intelligent Algorithm and Its Applications for Mobile Robot Control: A Survey

Bioinspired intelligent algorithm (BIA) is a kind of intelligent computing method, which is with a more lifelike biological working mechanism than other types. BIAs have made significant progress in both understanding of the neuroscience and biological systems and applying to various fields. Mobile robot control is one of the main application fields of BIAs which has attracted more and more attention, because mobile robots can be used widely and general artificial intelligent algorithms meet a development bottleneck in this field, such as complex computing and the dependence on high-precision sensors. This paper presents a survey of recent research in BIAs, which focuses on the research in the realization of various BIAs based on different working mechanisms and the applications for mobile robot control, to help in understanding BIAs comprehensively and clearly. The survey has four primary parts: a classification of BIAs from the biomimetic mechanism, a summary of several typical BIAs from different levels, an overview of current applications of BIAs in mobile robot control, and a description of some possible future directions for research

Surface Defect Target Identification on Copper Strip Based on Adaptive Genetic Algorithm and Feature Saliency

To enhance the stability and robustness of visual inspection system (VIS), a new surface defect target identification method for copper strip based on adaptive genetic algorithm (AGA) and feature saliency is proposed. First, the study uses gray level cooccurrence matrix (GLCM) and HU invariant moments for feature extraction. Then, adaptive genetic algorithm, which is used for feature selection, is evaluated and discussed. In AGA, total error rates and false alarm rates are integrated to calculate the fitness value, and the probability of crossover and mutation is adjusted dynamically according to the fitness value. At last, the selected features are optimized in accordance with feature saliency and are inputted into a support vector machine (SVM). Furthermore, for comparison, we conduct experiments using the selected optimal feature subsequence (OFS) and the total feature sequence (TFS) separately. The experimental results demonstrate that the proposed method can guarantee the correct rates of classification and can lower the false alarm rates