Robust set-membership state estimation; application to underwater robotics

This paper proposes a new observer for estimating the state vector of a nonlinear system. This observer, which is robust with respect to outliers, assumes that the measurement errors, as well as the number of outliers that could occur within a given time window, are bounded. The principle of the approach is to use interval analysis to deal properly with the nonlinearities involved in the system (without any linearization or approximation) and to propagate through the time, in a forward and backward manner, the assumptions made about outliers. A test case related to the localization and control of an underwater robot is also proposed to illustrate the efficiency of the approach.     

美国在华国际企业当地化问题研究

介绍了美国跨国企业在中国的投资概况，当地化举措，有待于国内企业的关注和借鉴。     

公司治理结构下财务总监的角色定位

论述了公司法人治理结构下，财务总监角色的定位发生了一系列的变化，新的公司治理框架下财务总监是战略计划领导者、战略管理财务支持系统的建设者、企业财务管理者、内部控制及监督者并对财务总监的素质提出了要求。     

基于最优信标节点的无线传感器网络质心定位算法

针对无线传感器网络(WSN)质心定位(CL)算法精度不高的问题,提出了一种基于最优信标节点的质心定位(OBCL)算法.OBCL算法采用了4个移动信标节点,首先,对移动信标节点的路径进行规划;然后,未知节点根据集合偏移度(SDD)从候选信标节点集合中选出最优信标节点来进行位置估计;其次,为了解决信标节点不足导致无法定位的问题,引入角色转变机制,未知节点在完成定位之后成为准信标节点来进行辅助定位;最后,为了保证网络中所有节点能够完成定位,在完成初次定位之后,需要进行重定位过程.仿真实验结果表明,与CL、基于加权的质心定位(WCL)、利用接收信号强度加权的质心定位(RR-WCL)这3种算法相比,OBCL算法的平均定位误差分别降低了67.7%、39.2%、24.4%.由于OBCL算法只需要4个移动信标节点就能达到较好的定位效果,因此适合应用于对网络成本低、定位精度要求高的场景.     

Localization for mobile target in wireless sensor networks

A precise localization for mobile target in wireless sensor networks is presented in this letter,where a geometrical relationship is explored to improve the location estimation for mobile target,instead of a simple centroid approach.The equations of location compensation algorithm for mobile target are derived based on linear trajectory prediction and sensor selective activation.The results based on extensive simulation experiments show that the compensation algorithm gets better performance in metrics of quality of tracking and energy efficiency with the change of sensor sensing range,the ratio of sensing range and sensor activation range,and the data sampling rate than traditional methods,which means our proposing can achieve better quality-energy tradeoff for mobile target in wireless sensor networks.     

Extended Monte Carlo localization algorithm for mobile sensor networks

A real-world localization system for wireless sensor networks that adapts for mobility and irregular radio propagation model is considered. The traditional range-based techniques and recent range-free localization schemes are not well competent for localization in mobile sensor networks, while the probabilistic approach of Bayesian filtering with particle-based density representations provides a comprehensive solution to such localization problem. Monte Carlo localization is a Bayesian filtering method that approximates the mobile node＇s location by a set of weighted particles. In this paper, an enhanced Monte Carlo localization algorithm-Extended Monte Carlo Localization （Ext-MCL） is proposed, i.e., the traditional Monte Carlo localization algorithm is improved and extended to make it suitable for the practical wireless network environment where the radio propagation model is irregular. Simulation results show the proposal gets better localization accuracy and higher localizable node number than previously proposed Monte Carlo localization schemes not only for ideal radio model, but also for irregular one.     

基于视觉FastSLAM的移动机器人自主探索方法*

针对室内环境下机器人的移动和定位需要,提出基于视觉FastSLAM的移动机器人自主探索方法.该方法综合考虑信息增益和路径距离,基于边界选取探索位置并规划路径,最大化机器人的自主探索效率,确保探索任务的完整实现.在FastSLAM 2.0的基础上,利用视觉作为观测手段,有效融合全景扫描和地标跟踪方法,提高数据观测效率,并且引入地标视觉特征增强数据关联估计,完成定位和地图绘制.实验表明,文中方法能正确选取最优探索位置并合理规划路径,完成探索任务,并且定位精度和地图绘制精度较高,鲁棒性较好.     

ONE UNIVERSITY ONE VILLAGE PROGRAM: PROMOTING ENDOGENOUS DEVELOPMENT IN RURAL AREAS THROUGH LOCAL PRACTICE

This interview is centered on the practice of earth architecture in rural West China, starting from introducing the post-earthquake reconstruction project in Guangming Village, Yunnan Province, as an outstanding example of the practice of the “One University, One Village” Rural Sustainable Development Assistance Program. Li Wan, the interviewee, explains the differences of design consideration and the difficulties in practicing earth architecture throughout China, while giving her prospect on its future application. Through a further introduction on the operation and project procedure of the Program by Wan and her team, the interviewee addresses that architects should take their responsibilities on solving social issues, with a hope that, through their site-based efforts, the Program could provide a global paradigm to similar earth architecture practices in other regions, rousing designers’ awareness of localization.     

绝对定位机制下目标位置估计辅助的群机器人搜索

基于扩展微粒群算法模型控制群机器人协同搜索目标时,成员机器人在社会经验和自身认知,主要是社会经验引导下逐步向目标趋近.由于社会经验仅从成员机器人的认知中“选举”产生,未形式化地融合多个机器人的经验,因此文中从群机器人通信子系统在本质上属于无线传感器网络的事实出发,引入集体决策机制,改进社会经验的生成模式.用无线传感器网络中的测距定位方法来估计目标位置,并将估计值作为社会经验引入现有模型.仿真结果表明,当群体规模够大时,采用文中社会经验生成模式可使协同搜索速度得到提高.     

基于无线传感网的海洋监测节点定位算法

针对应用于海洋监测的无线传感器网络,提出了一种基于蒙特卡罗算法的节点定位算法。该算法根据海洋中洋流在某一时间段内线性运动的特性,通过引入符合海水运动规律的角度来提高节点在位置预测阶段的精度;并根据节点感知压力的大小来确定预测坐标的置信度,以对预测坐标进行修正,进而得到最终的预测结果。仿真结果表明,该算法比传统的定位方法在不同的锚节点密度、节点密度、节点运动速度和时间等条件下都表现出更好的性能。