信息融合算法在机器人足球系统中的应用

最优信息融合Kalman滤波算法给出了实时动态环境中线性方差最小的融合估计。采用该算法对机器人足球系统中的小球进行状态估计和预测,并给出了信息融合处理结构和该算法的具体实现步骤。实验结果表明,该算法可以克服单一视觉传感器采集的数据含有较大噪声等局限性,实现了对小球精确的状态估计和预测,具有可行性和优越性,并且在某一机器人视觉传感器出错时,系统仍具有良好的容错性和鲁棒性。     

Multistage linear estimation using partitioning

The estimation algorithm described in this note solves the linear estimation problem as a two-stage estimator consisting of two consecutive Kalman filters. The interconnections between this estimator structure and the more familiar one-stage optimal Kalman filter are discussed. Applications to decentralized estimation, bias estimation, and parameter identification are described.     

The eXogenous Kalman Filter (XKF)

It is well known that the time-varying Kalman Filter (KF) is globally exponentially stable and optimal in the sense of minimum variance under some conditions. However, nonlinear approximations such as the extended KF linearises the system about the estimated state trajectories, leading in general to loss of both global stability and optimality. Nonlinear observers tend to have strong, often global, stability properties. They are, however, often designed without optimality objectives considering the presence of unknown measurement errors and process disturbances. We study the cascade of a global nonlinear observer with the linearised KF, where the estimate from the nonlinear observer is an exogenous signal only used for generating a linearised model to the KF. It is shown that the two-stage nonlinear estimator inherits the global stability property of the nonlinear observer, and simulations indicate that local optimality properties similar to a perfectly linearised KF can be achieved. This two-stage estimator is called an eXogeneous KF (XKF).     

广义系统Kalman融合器的研究

研究带自回归滑动平均(ARMA)有色观测噪声的多传感器广义离散随机线性系统,根据Kalman滤波方法和白噪声估计理论,在线性最小方差信息融合准则下,应用奇异值分解和增广状态空间模型,为了提高融合器的精度,提出了按矩阵加权降阶稳态广义Kalman融合器,可统一处理稳态滤波、平滑和预报问题,可减少计算负担和改善局部估计精度。并提出最优加权系数的局部估计误差方差和协方差阵的计算公式。用一个Monte Carlo数值仿真实例说明了所提方法的有效性。     

Optimal robot arm control using the minimum variance model

Models of human movement from computational neuroscience provide a starting point for building a system that can produce flexible adaptive movement on a robot. There have been many computational models of human upper limb movement put forward, each attempting to explain one or more of the stereotypical features that characterize such movements. While these models successfully capture some of the features of human movement, they often lack a compelling biological basis for the criteria they choose to optimize. One that does provide such a basis is the minimum variance model (and its extension—task optimization in the presence of signal‐dependent noise). Here, the variance of the hand position at the end of a movement is minimized, given that the control signals on the arm's actuators are subject to random noise with zero mean and variance proportional to the amplitude of the signal. Since large control signals, required to move fast, would have higher amplitude noise, the speed‐accuracy trade‐off emerges as a direct result of the optimization process. We chose to implement a version of this model that would be suitable for the control of a robot arm, using an optimal control scheme based on the discrete‐time linear quadratic regulator. This implementation allowed us to examine the applicability of the minimum variance model to producing humanlike movement. In this paper, we describe our implementation of the minimum variance model, both for point‐to‐point reaching movements and for more complex trajectories involving via points. We also evaluate its performance in producing humanlike movement and show its advantages over other optimization based models (the well‐known minimum jerk and minimum torque‐change models) for the control of a robot arm. © 2005 Wiley Periodicals, Inc.     

高效用模式产生策略综述

高效用模式挖掘用于从数据中找出对用户有用的信息。现有的高效用模式挖掘算法很多,如何选择更优的方法进行使用,是普遍存在的问题。要解决这个问题首先要了解高效用模式挖掘算法的分类,继而针对问题找出对应的算法。按照不同的角度可以划分多种不同类型的算法。从使用数据结构的类型,划分为基于树和基于效用列表的方法;从算法所需要经历的阶段,划分为一阶段和两阶段算法;还可以从算法使用的剪枝策略进行划分,如投影,保留效用,提高最小阈值等。首先对一阶段、两阶段高效用模式算法进行分析,主要分析基于树的两阶段算法和基于列表的一阶段算法。然后从是否产生候选分析基于树的高效用模式算法。最后分析高效用模式算法用到的缩减空间策略,如剪枝策略、投影技术等。通过分析得到一阶段算法在时间与空间上优于两阶段算法,不产生候选项集的算法在时间与空间上优于产生候选项集的算法,算法缩小搜索空间一般通过多种剪枝策略。     

On cluster-wise fuzzy regression analysis

Since Tanaka et al. (1982) proposed a study of linear regression analysis with a fuzzy model, fuzzy regression analysis has been widely studied and applied in a variety of substantive areas. Regression analysis in the case of heterogeneity of observations is commonly presented in practice. The authors' main goal is to apply fuzzy clustering techniques to fuzzy regression analysis. Fuzzy clustering is used to overcome the heterogeneous problem in the fuzzy regression model. They present the cluster-wise fuzzy regression analysis in two approaches: the two-stage weighted fuzzy regression and the one-stage generalized fuzzy regression. The two-stage procedure extends the results of Jajuga (1986) and Diamond (1988). The one-stage approach is created by embedding fuzzy clusterings into the fuzzy regression model fitting at each step of procedure. This kind of embedding in the one-stage procedure is more effective since the structure of regression line shape encountered in the data set is taken into account at each iteration of the algorithm. Numerical results give evidence that the one-stage procedure can be highly recommended in cluster-wise fuzzy regression analysis.     

Linear minimum variance estimation fusion

This paper shows that a general multisensor unbiased linearly weighted estimation fusion essentially is the linear minimum variance (LMV) estimation with linear equality constraint, and the general estimation fusion formula is developed by extending the Gauss-Markov estimation to the random parameter under estimation. First, we formulate the problem of distributed estimation fusion in the LMV setting. In this setting, the fused estimator is a weighted sum of local estimates with a matrix weight. We show that the set of weights is optimal if and only if it is a solution of a matrix quadratic optimization problem subject to a convex linear equality constraint. Second, we present a unique solution to the above optimization problem, which depends only on the covariance matrix Ck.Third, if a priori information, the expectation and covariance, of the estimated quantity is unknown, a necessary and sufficient condition for the above LMV fusion becoming the best unbiased LMV estimation with known prior informatio     

Optimal experiment design for linear systems with input-output constraints

The problem of optimal experiment design for parameter estimation in linear dynamic systems is studied. Results relating to both constrained input and output variances are established. For the case of constrained input variance, it is shown that a D-optimal experiment exists in which the system input is generated externally provided the system and noise transfer functions have no common parameters. For the case of constrained output variance, it is shown that an experiment in which the system input is generated by a combination of a minimum variance control law together with an external set point perturbation is D-optimal for certain classes of systems. Other related results are also presented which illustrate the role of feedback in optimal experiment design.     

Piecewise linear spine for speed–energy efficiency trade-off in quadruped robots

We compare the effects of linear and piecewise linear compliant spines on locomotion performance of quadruped robots in terms of energy efficiency and locomotion speed through a set of simulations and experiments. We first present a simple locomotion system that behaviorally resembles a bounding quadruped with flexible spine. Then, we show that robots with linear compliant spines have higher locomotion speed and lower cost of transportation in comparison with those with rigid spine. However, in linear case, optimal speed and minimum cost of transportation are attained at very different spine compliance values. Moreover, it is verified that fast and energy efficient locomotion can be achieved together when the spine flexibility is piecewise linear. Furthermore, it is shown that the robot with piecewise linear spine is more robust against changes in the load it carries. Superiority of piecewise linear spines over linear and rigid ones is additionally confirmed by simulating a quadruped robot in Webots and experiments on a crawling two-parts robot with flexible connection.     

The problem of state estimation via asynchronous communication channels with irregular transmission times

We study a linear discrete-time partially observed system perturbed by white noises. The observations are transmitted to the estimator via communication channels with irregular transmission times. Various measurement signals and even parts of a given sensor output may incur independent delays; messages transferred via the channels may be lost or corrupted. The minimum variance state estimation problem is solved. It is shown that the proposed state estimator is exponentially stable under natural assumptions.     

Minimum variance control and performance assessment of time-variant processes

This paper is concerned with (1) an explicit solution of a minimum variance control law for linear time-variant (LTV) processes in the transfer function form, and (2) performance assessment of LTV processes using minimum variance control as the benchmark. It is shown that there exists a time-variant, absolute lower bound of process variance that is achievable under LTV minimum variance control and can be estimated from routine operating data. This lower bound can subsequently be used to assess the benefit of implementing LTV control such as adaptive control. The proposed methods are illustrated through simulated examples and an industrial case study.     

数字高程模型中求趋势的一种方法：——无编最？…

在地理信息系统（ＧＩＳ）的数字高程模型中,经常需要求一些地形的趋势。本文给出一个求趋势的方法－－无偏最优求趋势法。根据某点周围若干个信息点上的地形高程数据估计出该点处的趋势值,而且这种估计 在满足线性、无偏、最小估计方差条件下求得的,这就是无偏最优求趋势法。它比经常用来求解势的方法－趋势分析方法,有许多优点。趋势分析方法只是无偏最优求趋势法的一个特例。     

Simulation-based two-stage estimation for multiple nonparametric regression

To reduce the curse of dimensionality arising from nonparametric estimation procedures for multiple nonparametric regression, in this paper we suggest a simulation-based two-stage estimation. We first introduce a simulation-based method to decompose the multiple nonparametric regression into two parts. The first part can be estimated with the parametric convergence rate and the second part is small enough so that it can be approximated by orthogonal basis functions with a small trade-off parameter. Then the linear combination of the first and second step estimators results in a two-stage estimator for the multiple regression function. Our method does not need any specified structural assumption on the regression function and it is proved that the newly proposed estimation is always consistent even if the trade-off parameter is designed to be small. Thus when the common nonparametric estimator such as local linear smoothing collapses because of the curse of dimensionality, our estimator still works well.     

Signal estimation for second-order vector difference equations

This note considers a linear estimation problem for a stochastic process viewed as the output signal of a linear second-order vector difference equation (VDE) driven by a white-noise input. An innovations approach is applied directly to develop the one-stage prediction estimator and associated error covariances. It is shown that the estimator can be expressed as a second-order recursion that preserves the mathematical structure of the given signal model with innovations feedback loops. It is also shown that the innovations can be computed through a first-order recursion in terms of one-stage prediction estimates and the measurements.     

Vision-based 3-D trajectory tracking for unknown environments

This paper describes a vision-based system for 3-D localization of a mobile robot in a natural environment. The system includes a mountable head with three on-board charge-coupled device cameras that can be installed on the robot. The main emphasis of this paper is on the ability to estimate the motion of the robot independently from any prior scene knowledge, landmark, or extra sensory devices. Distinctive scene features are identified using a novel algorithm, and their 3-D locations are estimated with high accuracy by a stereo algorithm. Using new two-stage feature tracking and iterative motion estimation in a symbiotic manner, precise motion vectors are obtained. The 3-D positions of scene features and the robot are refined by a Kalman filtering approach with a complete error-propagation modeling scheme. Experimental results show that good tracking and localization can be achieved using the proposed vision system.     

Algorithms and complexity results for graph-based pursuit evasion

We study the classical edge-searching pursuit-evasion problem where a number of pursuers have to clear a given graph of fast-moving evaders despite poor visibility, for example, where robots search a cave system to ensure that no terrorists are hiding in it. We study when polynomial-time algorithms exist to determine how many robots are needed to clear a given graph (minimum robot problem) and how a given number of robots should move on the graph to clear it with either a minimum sum of their travel distances (minimum distance problem) or minimum task-completion time (minimum time problem). The robots cannot clear a graph if the vertex connectivity of some part of the graph exceeds the number of robots. Researchers therefore focus on graphs whose subgraphs can always be cut at a limited number of vertices, that is, graphs of low treewidth, typically trees. We describe an optimal polynomial-time algorithm, called CLEARTHETREE, that is shorter and algorithmically simpler than the state-of-the-art algorithm for the minimum robot problem on unit-width unit-length trees. We then generalize prior research to both unit-width arbitrary-length and unit-length arbitrary-width graphs and derive both algorithms and time complexity results for a variety of graph topologies. Pursuit-evasion problems on the former graphs are generally simpler than pursuit-evasion problems on the latter graphs. For example, the minimum robot and distance problems are solvable in polynomial time on unit-width arbitrary-length trees but NP-hard on unit-length arbitrary-width trees.     

Error growth in position estimation from noisy relative pose measurements

We examine how the estimation error grows with time when a mobile robot estimates its location from relative pose measurements without global position or orientation sensors. We show that, in both two-dimensional and three-dimensional space, both the bias and the variance of the position estimation error grows at most linearly with time asymptotically. Non-asymptotic bounds on the bias and variance are obtained, which provide insight into the mechanism of error growth. The bias is crucially dependent on the trajectory of the robot. Conclusions on the asymptotic growth rate of the bias continue to hold even with unbiased measurements or error-free translation measurements. Exact formulas for the bias and the variance of the position estimation error are provided for two specific two-dimensional trajectories–straight line and periodic. Experiments with a P3-DX wheeled robot and Monte Carlo simulations are provided to verify the theoretical predictions. A method to reduce the bias is proposed based on the lessons learned.     

Robust self-tuning control based on discrete-time sliding mode for auto-regressive mathematical model in the presence of unmodelled dynamics

In this paper, we propose a new robust self-tuning control, called the generalized minimum variance αl-equivalent selftuning control (GMVSTC-αl) for the linear timevarying (LTV) systems, which can be described by the discrete-time auto-regressive exogenous (ARX) mathematical model in the presence of unmodelled dynamics. The estimation of the parameters contained in this mathematical model is made on the basis of the proposed modified recursive least squares (m-RLS) parametric estimation algorithm with dead zone and forgetting factor. The stability analysis of the proposed parametric estimation algorithm m-RLS is treated on the basis of a Lyapunov function. A numerical simulation example is used to prove the performances and the effectiveness of the explicit scheme of the proposed robust self-tuning control GMVSTC-αl.