复杂网络的事件驱动故障估计

针对一类非线性耦合的复杂网络系统，提出了一种基于复杂网络估计器的近似最优故障估计方法.首先将复杂网络的状态与故障进行增广，然后对增广后的状态和故障进行了联合状态估计.为了处理多信号传输可能发生的数据冲突，采用了事件驱动的方法使复杂网络的输出传输至远程估计器.通过递推矩阵方程方法给出了估计误差协方差矩阵的上界，并通过设计估计器参数使得该上界在迹的意义下最小.最后，通过仿真例子验证了所提联合估计方案的可行性和有效性.     

Kinematic Modeling and Analysis of Skid-Steered Mobile Robots With Applications to Low-Cost Inertial-Measurement-Unit-Based Motion Estimation

Skid-steered mobile robots are widely used because of their simple mechanism and high reliability. Understanding the kinematics and dynamics of such a robotic platform is, however, challenging due to the complex wheel/ground interactions and kinematic constraints. In this paper, we develop a kinematic modeling scheme to analyze the skid-steered mobile robot. Based on the analysis of the kinematics of the skid-steered mobile robot, we reveal the underlying geometric and kinematic relationships between the wheel slips and locations of the instantaneous rotation centers. As an application example, we also present how to utilize the modeling and analysis for robot positioning and wheel slip estimation using only low-cost strapdown inertial measurement units. The robot positioning and wheel slip-estimation scheme is based on an extended Kalman filter (EKF) design that incorporates the kinematic constraints for accuracy enhancement. The performance of the EKF-based positioning and wheel slip-estimation scheme are also presented. The estimation methodology is tested and validated experimentally on a robotic test bed.     

含驱动器动力学的移动机器人编队自适应控制

针对含有驱动器及编队动力学的多非完整移动机器人编队控制问题,基于领航者-跟随者[l-ψ]控制结构,通过反步法设计了一种将运动学控制器与驱动器输入电压控制器相结合的新型控制策略。采用径向基神经网络（RBFNN）对跟随者及领航者动力学非线性不确定部分进行在线估计,并通过自适应鲁棒控制器对神经网络建模误差进行补偿。该方法不但解决了移动机器人编队控制的参数与非参数不确定性问题,同时也确保了机器人编队在期望队形下对指定轨迹的跟踪;基于Lyapunov方法的设计过程,保证了控制系统的稳定与收敛;仿真结果表明了该方法的有效性。     

Sensor Fusion for Joint Kinematic Estimation in Serial Robots Using Encoder,Accelerometer and Gyroscope

Open-chain manipulator robots play an important role in the industry, since they are utilized in applications requiring precise motion. High-performance motion of a robot system mainly relies on adequate trajectory planning and the controller that coordinates the movement. The controller performance depends of both, the employed control law and the sensor feedback. Optical encoder feedback is the most used sensor for angular position estimation of each joint in the robot, since they feature accurate and low noise angular position measurements. However, it cannot detect mechanical imperfections and deformations common in open chain robots. Moreover, velocity and acceleration cannot be extracted from the encoder data without adding phase delays. Sensor fusion techniques are found to be a good solution for solving this problem. However, few works has been carried out in serial robots for kinematic estimation of angular position, velocity and acceleration, since the delays induced by the filtering techniques avoids its use as controller feedback. This work proposes a novel sensor-fusion-based feedback system capable of providing complete kinematic information from each joint in 4-degrees of freedom serial robot, with the contribution of a proposed methodology based on Kalman filtering for fusing the information from optical encoder, gyroscope and accelerometer appended to the robot. Calibration and experimentation are carried out for validating the proposal. The results are compared with another kinematic estimation technique finding that this proposal provides more information about the robot movement without adding state delays, which is important for being used as controller feedback.     

Kinematic analysis and error modeling of TAU parallel robot

The TAU robot presents a new configuration of parallel robots with three degrees of freedom. This robotic configuration is well adapted to perform with a high precision and high stiffness within a large working range compared with a serial robot. It has the advantages of both parallel robots and serial robots. In this paper, the kinematic modeling and error modeling are established with all errors considered using Jacobian matrix method for the robot. Meanwhile, a very effective Jacobian approximation method is introduced to calculate the forward kinematic problem instead of Newton–Raphson method. It denotes that a closed form solution can be obtained instead of a numerical solution. A full size Jacobian matrix is used in carrying out error analysis, error budget, and model parameter estimation and identification. Simulation results indicate that both Jacobian matrix and Jacobian approximation method are correct and with a level of accuracy of micron meters. ADAMS's simulation results are used in verifying the established models.     

An uncertainty compensator for robust control of wheeled mobile robots

This paper proposes an uncertainty compensator to design a novel robust control for mobile robots with dynamic and kinematic uncertainties. A novel gradient-based adaptive fuzzy estimator is developed to compensate uncertainties with minimum required feedback signals. As a novelty, the proposed approach uses the tracking error and its first time derivative to form the estimation error of uncertainty, and guarantees that both the estimation error and tracking error converge asymmetrically to ignorable value. Advantages of the proposed robust control are simplicity in design, robustness against uncertainties, guaranteed stability, and good control performance. The control approach is verified by stability analysis. Simulation results and experimental results illustrate the effectiveness of the proposed control. Experimental evaluation of the proposed controller is expressed for two different low-cost nonholonomic wheeled mobile robots. The proposed control design is compared with an adaptive control approach to confirm the superiority of the proposed approach in terms of precision, simplicity of design, and computations.     

Robotic Hand Pose Estimation Based on Stereo Vision and GPU-enabled Internal Graphical Simulation

Humanoid robots have complex kinematic chains whose modeling is error prone. If the robot model is not well calibrated, its hand pose cannot be determined precisely from the encoder readings, and this affects reaching and grasping accuracy. In our work, we propose a novel method to simultaneously i) estimate the pose of the robot hand, and ii) calibrate the robot kinematic model. This is achieved by combining stereo vision, proprioception, and a 3D computer graphics model of the robot. Notably, the use of GPU programming allows to perform the estimation and calibration in real time during the execution of arm reaching movements. Proprioceptive information is exploited to generate hypotheses about the visual appearance of the hand in the camera images, using the 3D computer graphics model of the robot that includes both kinematic and texture information. These hypotheses are compared with the actual visual input using particle filtering, to obtain both i) the best estimate of the hand pose and ii) a set of joint offsets to calibrate the kinematics of the robot model. We evaluate two different approaches to estimate the 6D pose of the hand from vision (silhouette segmentation and edges extraction) and show experimentally that the pose estimation error is considerably reduced with respect to the nominal robot model. Moreover, the GPU implementation ensures a performance about 3 times faster than the CPU one, allowing real-time operation.     

基于神经网络的一类生化反应器状态在线估计

提出一种随机非线性系统状态和参数同时估计的神经网络新方法，并证明了该方法的无偏性和是小方差性，将其用于乙醇间歇发酵器的状态和参数估计，结果表明估计值民实验值相吻合，此方法对噪声特片无特殊要求，对初始状态估值不敏感，对初始参数值具有一定的鲁棒性，可利用有限的状态量测信息在线估计不可测量的状态变量和物理参数。     

具有不确定动态线性系统的鲁棒状态估计

本文研究了一类具有参数和噪声特性不确定线性系统的鲁棒状态估计问题。利用对策论思想,定义了能使不确定下最坏性能最好的极小极大鲁棒状态估计器,提出了一种简单的近似设计方法,即设计最坏对象的最优滤波器。给出了这种设计方法设计滤波器导致的性能误差边界,进一步指出当满足文中给出的鞍点条件时,最坏对象的最优滤波器就是极小极大鲁棒滤波器。     

Model‐based Prediction of Skid‐steer Robot Kinematics Using Online Estimation of Track Instantaneous Centers of Rotation

This paper presents a kinematic extended Kalman filter (EKF) designed to estimate the location of track instantaneous centers of rotation (ICRs) and aid in model‐based motion prediction of skid‐steer robots. Utilizing an ICR‐based kinematic model has resulted in impressive odometry estimates for skid‐steer movement in previous works, but estimation of ICR locations was performed offline on recorded data. The EKF presented here utilizes a kinematic model of skid‐steer motion based on ICR locations. The ICR locations are learned by the filter through the inclusion of position and heading measurements. A background on ICR kinematics is presented, followed by the development of the ICR EKF. Simulation results are presented to aid in the analysis of noise and bias susceptibility. The experimental platforms and sensors are described, followed by the results of filter implementation. Extensive field testing was conducted on two skid‐steer robots, one with tracks and another with wheels. ICR odometry using learned ICR locations predicts robot position with a mean error of ?0.42 m over 40.5 m of travel during one tracked vehicle test. A test consisting of driving both vehicles approximately 1,000 m shows clustering of ICR estimates for the duration of the run, suggesting that ICR locations do not vary significantly when a vehicle is operated with low dynamics.     

Simultaneous Detection and Estimation Approach for Speech Enhancement

In this paper, we present a simultaneous detection and estimation approach for speech enhancement. A detector for speech presence in the short-time Fourier transform domain is combined with an estimator, which jointly minimizes a cost function that takes into account both detection and estimation errors. Cost parameters control the tradeoff between speech distortion, caused by missed detection of speech components and residual musical noise resulting from false-detection. Furthermore, a modified decision-directed a priori signal-to-noise ratio (SNR) estimation is proposed for transient-noise environments. Experimental results demonstrate the advantage of using the proposed simultaneous detection and estimation approach with the proposed a priori SNR estimator, which facilitate suppression of transient noise with a controlled level of speech distortion.     

基于改进遗传算法的信道估计方案

为了提高通信系统信道估计的准确率,同时适应更大的数据量,进行更加复杂的数据计算,引入神经网络的方法进行信道估计,采用了BP和RBF神经网络进行实验对比,与传统信道估计方式相比有明显提升;在此基础上,进一步提出基于改进遗传算法优化的 RBF 神经信道估计方法,目的是帮助确定 RBF 网络的隐藏层参数, 使得网络的参数趋于全局最优解,信道估计器的性能从而得到提升。经过 MATLAB 仿真,改进后的RBF神经网络可以更好地解决信道估计问题,验证了此方法的可行性。     

Deterministic Learning for Maximum-Likelihood Estimation Through Neural Networks

In this paper, a general method for the numerical solution of maximum-likelihood estimation (MLE) problems is presented; it adopts the deterministic learning (DL) approach to find close approximations to ML estimator functions for the unknown parameters of any given density. The method relies on the choice of a proper neural network and on the deterministic generation of samples of observations of the likelihood function, thus avoiding the problem of generating samples with the unknown density. Under mild assumptions, consistency and convergence with favorable rates to the true ML estimator function can be proved. Simulation results are provided to show the good behavior of the algorithm compared to the corresponding exact solutions.      

Joint High-Resolution Fundamental Frequency and Order Estimation

In this paper, we present a novel method for joint estimation of the fundamental frequency and order of a set of harmonically related sinusoids based on the multiple signal classification (MUSIC) estimation criterion. The presented method, termed HMUSIC, is shown to have an efficient implementation using fast Fourier transforms (FFTs). Furthermore, refined estimates can be obtained using a gradient-based method. Illustrative examples of the application of the algorithm to real-life speech and audio signals are given, and the statistical performance of the estimator is evaluated using synthetic signals, demonstrating its good statistical properties.     

Joint origin identification of articulated robots with marker-based multi-camera optical tracking systems

Marker-based multi-camera optical tracking systems are being used in the robotics field to track robots for validation, verification, and calibration of their kinematic and dynamic models. These tracking systems estimate the pose of tracking bodies attached to objects within a tracking volume. In this work, we explore the case of tracking the origins of joints of articulated robots when the tracking bodies are mounted on limbs or structures relative to the joints. This configuration leads to an unknown relative pose between the tracking body and the joint origin. The identification of this relative pose is essential for an accurate representation of the kinematic model. We propose an approach for the identification of the origin of joints relative to tracking bodies by using state-of-the-art center of rotation (CoR) and axis of rotation (AoR) estimation methods. The applicability and effectiveness of our approach is demonstrated in two successful case studies: (i) the verification of the upper body kinematics of DLR’s humanoid Rollin’ Justin and (ii) the identification of the kinematic parameters of an ST Robot arm relative to its environment for the embodiment of a situated conversational assistant.     

地磁传感器误差参数估计与补偿方法

地磁传感器误差参数通常在事前标定校准,但校准参数在长时间的置放后,或者应用环境的发生改变时,地磁传感器校准参数将会发生变化,从而造成磁测补偿效果并不理想。为期解决上述问题,本文提出了基于滤波技术的地磁传感器误差参数估计与补偿方法。仿真结果表明该方法是可行的,最为重要的是磁传感器通过参数估计与磁测补偿后,其测量精度最少提高了一个数量级。     

INTELLIGENT MOTION CONTROL FOR OMNIDIRECTIONAL MOBILE ROBOTS USING ANT COLONY OPTIMIZATION

This article presents an intelligent system-on-a-programmable-chip-based (SoPC) ant colony optimization (ACO) motion controller for embedded omnidirectional mobile robots with three independent driving wheels equally spaced at 120 degrees from one another. Both ACO parameter autotuner and kinematic motion controller are integrated in one field-programmable gate array (FPGA) chip to efficiently construct an experimental mobile robot. The optimal parameters of the motion controller are obtained by minimizing the performance index using the proposed SoPC-based ACO computing method. These optimal parameters are then employed in the ACO-based embedded kinematic controller in order to obtain better performance for omnidirectional mobile robots to achieve trajectory tracking and stabilization. Experimental results are conducted to show the effectiveness and merit of the proposed intelligent ACO-based embedded controller for omnidirectional mobile robots. These results indicate that the proposed ACO-based embedded optimal controller outperforms the nonoptimal controllers and the conventional genetic algorithm (GA) optimal controllers.     

Estimation model for kinematic calibration of manipulators with a parallel structure

This article provides an estimation model for calibrating the kinematics of manipulators with a parallel geometrical structure. Parameter estimation for serial link manipulators is well developed, but fail for most structures with parallel actuators, because the forward kinematics is usually not analytically available for these. We extend parameter estimation to such parallel structures by developing an estimation method where errors in kinematical parameters are linearly related to errors in the tool pose, expressed through the inverse kinematics, which is usually well known. The method is based on the work done to calibrate the MultiCraft robot. This robot has five linear actuators built in parallel around a passive serial arm, thus making up a two-layered parallel-serial manipulator, and the unique MultiCraft construction is reviewed. Due to the passive serial arm, for this robot conventional serial calibration must be combined with estimation of the parameters in the parallel actuator structure. The developed kinematic calibration method is verified through simulations with realistic data and real robot kinematics, taking the MultiCraft manipulator as the case. © 1994 John Wiley & Sons, Inc.     

Estimation fusion for distributed multi-sensor systems with uncertain cross-correlations

This paper addresses the estimation fusion problem in distributed multi-sensor systems with uncertain cross-covariance among local estimation errors. A robust linear estimation fusion method is proposed in the sense of minimising the worst mean square error of the fused estimator over the uncertain normalised cross-covariances (NCC). The weighted coefficient matrices of the fused estimator can be obtained by solving a semi-definite programming problem. This estimation fusion method is suitable for the situations with completely unknown NCC or partly known NCC. Two fusion estimators for the uncertain NCC with partly known prior information are presented. Some numerical simulations are provided to show the good performance of the proposed estimators.