基于遗传算法的AS-R移动机器人路径规划研究

将遗传算法应用于机器人的全局路径规划,并在AS-R移动机器人上进行了实验研究.首先用栅格法对机器人的工作环境进行划分,得到机器人的环境模型;其次采用遗传算法进行路径搜索,并引入多种遗传算子,增强了算法的实用性.在AS-R机器人上采用VC开发,并在5×5的栅格环境中进行路径规划研究.实验结果表明,遗传算法结合栅格环境对移动机器人进行路径规划,具有简单且通用的效果,所得的折线路径也更适合于机器人进行轨迹跟踪.     

Real-time hierarchical stereo Visual SLAM in large-scale environments

In this paper we present a new real-time hierarchical (topological/metric) Visual SLAM system focusing on the localization of a vehicle in large-scale outdoor urban environments. It is exclusively based on the visual information provided by a cheap wide-angle stereo camera. Our approach divides the whole map into local sub-maps identified by the so-called fingerprints (vehicle poses). At the sub-map level (low level SLAM), 3D sequential mapping of natural landmarks and the robot location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A higher topological level (high level SLAM) based on fingerprints has been added to reduce the global accumulated drift, keeping real-time constraints. Using this hierarchical strategy, we keep the local consistency of the metric sub-maps, by mean of the EKF, and global consistency by using the topological map and the MultiLevel Relaxation (MLR) algorithm. Some experimental results for different large-scale outdoor environments are presented, showing an almost constant processing time.     

Guaranteed cost control analysis and iterative design for constrained Takagi–Sugeno systems

This paper presents a novel control design technique in order to obtain a guaranteed cost fuzzy controller subject to constraints on the input channel. This guaranteed cost control law is obtained via multi-parametric quadratic programming. The result is a piecewise fuzzy control law where the state partition is defined by fuzzy inequalities. The parameters of the Lyapunov function can be obtained previously using Linear Matrix Inequalities optimization.     

Two-stage cascaded classification approach based on genetic fuzzy learning for speech/music discrimination

Automatic discrimination of speech and music is an important tool in many multimedia applications. The paper presents a robust and effective approach for speech/music discrimination, which relies on a two-stage cascaded classification scheme. The cascaded classification scheme is composed of a statistical pattern recognition classifier followed by a genetic fuzzy system. For the first stage of the classification scheme, other widely used classifiers, such as neural networks and support vector machines, have also been considered in order to assess the robustness of the proposed classification scheme. Comparison with well-proven signal features is also performed. In this work, the most commonly used genetic learning algorithms (Michigan and Pittsburgh) have been evaluated in the proposed two-stage classification scheme. The genetic fuzzy system gives rise to an improvement of about 4% in the classification accuracy rate. Experimental results show the good performance of the proposed approach with a classification accuracy rate of about 97% for the best trial.     

A natural-disaster management DSS for Humanitarian Non-Governmental Organisations

Humanitarian Non-Governmental Organisations (NGOs) play a growing role in the response to natural disasters, but despite being largely demanded, there is no available decision support system (DSS) specifically designed to address their problem. In this paper we present a decision support system (DSS) to aid those Humanitarian NGOs concerned with the response to natural disasters. Such a DSS has been designed avoiding sophisticated methodologies that may exceed the infrastructural requirements and constraints of emergency management by NGOs. A data-based, two-level knowledge methodology which allows damage assessment of multiple disaster scenarios is presented in order to address that problem. Validation results show viability of our approach.     

Simultaneous algorithm for trajectory planning

This paper addresses the solution of smooth trajectory planning for industrial robots in environments with obstacles using a direct method, creating the trajectory gradually as the robot moves. The presented method deals with the uncertainties associated with the lack of knowledge of kinematic properties of intermediate via‐points since they are generated as the algorithm evolves looking for the solution. Several cost functions are also proposed, which use the time that has been calculated to guide the robot motion. The method has been applied successfully to a PUMA 560 robot and four operational parameters (execution time, computational time, distance travelled and number of configurations) have been computed to study the properties and influence of each cost function on the trajectory obtained. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

云重心评判法在防空兵作战能力评估中的应用

在传统的云重心评判法中,加权偏离度不符合"非负有界性、可加性、归一性"的测度准则,给评测 结果的生成带来了较大困难;鉴于此,提出一种改进的云重心评判法,该方法借鉴TOPSIS法的基本思想,基于理想状态和负理想状态,对综合云重心向量进行归一化,由此得出的修正的加权偏离度符合"非负有界性、可加性、归一性"的测度准则,从而克服了传统云重心评判法的不足;以防空兵作战能力评估为例,应用实例证明了改进的云重心评判法的合理性和有效性.     

利用无线传感器网络传输RFID数据的研究

利用无线传感器网络高可靠性、低成本、低复杂度的特点,实现了一个UHF RFID数据传输网络;首先介绍了UHF RFID系统和无线传感器网络的结构;采用高性能的无线单片机CC2430,通过建立和维护设备地址映射表,来实现一个无线串口网络;测试了无线串口网络传输的性能;结果显示,无线传感器网络是一个性价比很高的UHF RFID数据传输手段,是对有线传输很好的补充.     

基于动态任务菜单的RBAC模型的设计与实现

针对RBAC模型存在的两大问题一访问权限粒度与角色规模的矛盾和角色被动、缺乏灵活的授权机制,提出基于动态任务菜单的RBAC-DTM_RBAC模型,引入动态菜单和任务的概念,通过对角色的静态约束和任务的动态权限管理,很好地克服了角色冗余的问题,减少角色规模,降低角色管理的复杂性,更好适应多级复杂权限系统的动态权限管理要求。