Development of a parallel wire mechanism for measuring position and orientation of a robot end-effector

This paper presents a parallel wire mechanism developed for measuring six degrees of freedom of a robot end-effector. The mechanism consists of six parallel wire links. The position and orientation of a robot are obtained from the wire lengths measured in the parallel wire mechanism. The complex nonlinear equations of the forward kinematics are solved by using a Newton–Raphson method, and a unique solution is determined from the geometric configuration of the developed mechanism. The wire length error caused by longitudinal deformation of the wires is compensated by a wire compensation factor. Through the experiment, it is verified that the developed mechanism has an accuracy of ±0.05 mm, ±0.1° in the position and the orientation, respectively. The developed parallel wire mechanism can be used effectively for measuring the position and orientation of a six degree of freedom (DOF) of robot end-effector with low cost and effort.     

Ambulatory position and orientation tracking fusing magnetic and inertial sensing

This paper presents the design and testing of a portable magnetic system combined with miniature inertial sensors for ambulatory 6 degrees of freedom (DOF) human motion tracking. The magnetic system consists of three orthogonal coils, the source, fixed to the body and 3-D magnetic sensors, fixed to remote body segments, which measure the fields generated by the source. Based on the measured signals, a processor calculates the relative positions and orientations between source and sensor. Magnetic actuation requires a substantial amount of energy which limits the update rate with a set of batteries. Moreover, the magnetic field can easily be disturbed by ferromagnetic materials or other sources. Inertial sensors can be sampled at high rates, require only little energy and do not suffer from magnetic interferences. However, accelerometers and gyroscopes can only measure changes in position and orientation and suffer from integration drift. By combing measurements from both systems in a complementary Kalman filter structure, an optimal solution for position and orientation estimates is obtained. The magnetic system provides 6 DOF measurements at a relatively low update rate while the inertial sensors track the changes position and orientation in between the magnetic updates. The implemented system is tested against a lab-bound camera tracking system for several functional body movements. The accuracy was about 5 mm for position and 3 degrees for orientation measurements. Errors were higher during movements with high velocities due to relative movement between source and sensor within one cycle of magnetic actuation.     

Joint time-delay estimation and adaptive recursive least squaresfiltering

A general estimation model is defined in which two observations are available: a noisy and a noise-filtered and delayed version of the transmitted signal. The delay and the filter must be estimated. The joint estimator is composed of an adaptive delay element operating in conjunction with an adaptive transversal filter. The delay is updated using a form of derivative, with respect to the delay, of the sum of squared errors. The adaptive delay is limited to integer values and is defined as the lag. The lag value is computed and updated so that the optimum least-squares solution is attained. The joint algorithm is obtained by combining the lag update relations with a version of the fast transversal filter RLS algorithm. Simulations show that both stationary and time-varying delays are effectively tracked and that the adaptive filter properly estimates the reference filter impulse response     

Multi-hierarchy interaction control of a redundant robot using impedance learning

The control of robots with a compliant joint motion is important for reducing collision forces and improving safety during human robot interactions. In this paper, a multi-hierarchy control framework is proposed for the redundant robot to enable the robot end-effector to physically interact with the unknown environment, while providing compliance to the joint space motion. To this end, an impedance learning method is designed to iteratively update the stiffness and damping parameters of the end-effector with desired performance. In addition, based on a null space projection technique, an extra low stiffness impedance controller is included to improve compliant joint motion behaviour when interaction forces are acted on the robot body. With an adaptive disturbance observer, the proposed controller can achieve satisfactory performance of the end-effector control even with the external disturbances in the joint space. Experimental studies on a 7 DOF Sawyer robot show that the learning framework can not only update the target impedance model according to a given cost function, but also enhance the task performance when interaction forces are applied on the robot body.     

Error analysis and control scheme for the error correction in trajectory-tracking of a planar 2PRP-PPR parallel manipulator

This paper presents the end-effector pose error modeling and motion accuracy analysis of a planar 2PRP-PPR parallel manipulator with an unsymmetrical (U-shape) fixed base. The error model is established based on the screw theory with considerations of both configuration (geometrical) errors and joint clearances. It also proposes a robust cascaded control scheme for the end-effector pose (task-space) error correction in trajectory-tracking of the manipulator due to mechanical inaccuracies. The proposed control scheme uses redundant sensor feedback, i.e., individual active joint displacements and velocities and, end-effector positions and orientation are obtained as feedback signals using appropriate sensors. To demonstrate the efficacy and show complete performance of the proposed controller, real-time experiments are accomplished on an in-house fabricated planar 2PRP-PPR parallel manipulator.     

Parallel structures for joint channel estimation and data detectionover fading channels

Joint data and channel estimation for mobile communication receivers can be realized by employing a Viterbi detector along with channel estimators which estimate the channel impulse response. The behavior of the channel estimator has a strong impact on the overall error rate performance of the receiver. Kalman filtering is an optimum channel estimation technique which can lead to significant improvement in the receiver bit error rate (BER) performance. However, a Kalman filter is a complex algorithm and is sensitive to roundoff errors. Square-root implementation methods are required for robustness against numerical errors. Real-time computation of the Kalman estimator in a mobile communication receiver calls for parallel and pipelined structures to take advantage of the inherent parallelism in the algorithm. In this paper different implementation methods are considered for measurement update and time update equations of the Kalman filter. The unit-lower-triangular-diagonal (LD) correction algorithm is used for the time update equations, and systolic array structures are proposed for its implementation. For the overall implementation of joint data and channel estimation, parallel structures are proposed to perform both the Viterbi algorithm and channel estimation. Simulation results show the numerical stability of different implementation techniques and the number of bits required in the digital computations with different estimators     

Online statistical model recognition and State estimation for autonomous compliant motion

Autonomous execution of robot tasks requires the ability to deal online with uncertainties such as partially unknown environments, inaccurate models, and measurement noise. This is especially true for the execution of motions maintaining stiff contacts ("compliant motions"), as contact forces become very high even for small position errors. The autonomy during compliant motion tasks is based on i) a force controller, dealing with small misalignments and keeping the contact forces within safe limits, and ii) an estimator, which recognizes the model (e.g., the type of contact) and estimates the system state (e.g., the relative position of the contacting objects). This paper focuses on Bayesian model-based solutions to the model recognition problem. We discuss Bayesian hypothesis testing and practical approximations. Experimental results are provided for two autonomous-compliant motion tasks by applying consistency testing and likelihood ratio testing. The system state is estimated simultaneously with the model recognition. This estimation is performed by the Iterated Extended Kalman filter for (approximate) linear problems and by the nonminimal state Kalman filter for nonlinear problems.     

针对临近空间目标跟踪的自适应AMCKF算法

临近空间飞行器具有机动特性复杂、运动轨迹多阶段性等特点,在目标跟踪的过程中,易出现由于系统模型误差较大导致跟踪精度降低、滤波发散的问题。针对该问题,在容积卡尔曼滤波的过程中加入衰减因子,通过衰减记忆的方法补偿模型误差;同时,提出了一种实时辨识容积卡尔曼滤波衰减因子的方法,达到自适应跟踪的目的。仿真结果表明:衰减记忆容积卡尔曼滤波算法能够很好地解决模型失配问题,自适应算法实时对衰减因子赋值,避免了衰减因子取值的困难,可以达到更好的跟踪效果。     

Estimation of linearly constrained parameters via the Kalman filter

The estimation of a least-squares parameter (or state) vector in a standard model is extended to the least-squares estimation of a linearly constrained vector. It is shown that the required pseudoinverses can be computed recursively, and, with suitable initialisation, a standard Kalman filter will provide the same estimates. The resulting recursion is a powerful starting point in the design of adaptive communications filters and antennas.     

一种基于预测值量测转换的卡尔曼滤波跟踪算法

在雷达目标跟踪中,系统量测信息通常在球坐标系下获得。为了采用经典卡尔曼滤波算法实现有效目标跟踪,通常采用量测转换方法将非线性量测信息转换到直角坐标系中。针对传统量测转换方法基于量测值计算转换误差统计特性而导致的估计结果有偏问题,提出了一种基于预测值的量测转换方法,并将其与卡尔曼滤波算法相结合,获得了一种基于预测值量测转换的卡尔曼滤波跟踪算法。仿真结果表明,与现有的基于量测转换的卡尔曼滤波算法相比,该算法能在不提高运算量的情况下有效改善目标跟踪效果,跟踪精度提升约20%。     

基于卡尔曼滤波的无线传感网时空数据融合算法

无线传感网络节点采集的信息具有较大的相似性,数据结果存在误差。针对该问题,文中提出了一种基于卡尔曼滤波的无线传感网数据融合算法,通过过滤无效数据和缩紧数据包,提高上传数据的有效性和精度。该算法采用实时性较高的卡尔曼滤波算法对无线传感网络中的数据根据时间序列进行数据融合。在时间数据融合的基础上,根据空间分布特点,进一步对多传感器在网关层依据权重进行数据融合。针对不同位置误差实时变化的特点,网关层以空间数据为基础,使用自适应加权算法动态调整各节点权重。仿真实验表明,该算法易于实现,可有效去除冗余信息,提高数据准确度和可靠性。相较于改进的分批估计与自适应加权方法,采用该方法后均方根误差减少约7.9%,精度提高了2.1%。     

An Improved Adaptive Predictor in DPCM Based on the Kalman Filter and Its Application to Handwriting Signal Encoding

A method of handwriting signal encoding based on adaptive linear predictive coding (ALPC) is studied. The ALPC is a form of DPCM which uses a sequentially adaptive predictor in which a sequential estimation algorithm is used to update predictor coefficients. To improve the estimates of the predictor coefficients in the presence of quantization noise, Kalman filtering has been investigated for its feasibility. This results in improvements of not only the estimation of the predictor coefficients, but the signal-to-quantization-noise ratio (SNR) of the signals reconstructed at the receiver as well. Computer simulations have verified that the ALPC system employing the Kalman filter promises high performance and feasibility at the rate of 192 bits/s when applied to handwriting signal encoding.     

An adaptive controller for a direct-drive SCARA robot

A direct adaptive controller for trajectory tracking of high-speed robots such as a direct-drive SCARA robot is presented. In this robot, nonlinear effects due to centrifugal, Coriolis, and inertial forces are more important than friction and gravity forces, unlike most industrial robots. The control law of the adaptive scheme consists of a PD regulator plus feedforward compensation of full dynamics. The feedforward terms are adjusted by an adaptation law so that the steady-state position errors are zero. With this adaptive controller, the joint acceleration measurement is not required and no inversion of the estimated mass matrix is involved. The tracking performances of the controller applied to a two-degree-of-freedom SCARA is illustrated by a real-time implementation based on a single-chip digital signal processor (DSP)     

用于机动目标跟踪的Kalman滤波器的设计

机动目标跟踪广泛应用于军事和民用领域。本文针对机动目标跟踪问题,在“当前”统计模型的基础上,实现了Kalman滤波算法在工程上的应用;同时利用速度预测估计与实时速度估计间的偏差进行自适应方差调整,提出了改进的“当前”统计模型自适应滤波算法。工程实践表明,基于改进模型的Kalman滤波算法在跟踪机动目标时具有起好的跟踪性能,同时也极大改善了对一般非机动目标的跟踪能力。     

基于高维状态空间的移动机器人定位

位置跟踪是移动机器人自主导航中的一个主要任务.扩展的卡尔曼滤波定位方法是一个常用的位置跟踪方法,但是在对非线性系统方程进行线性化近似过程中引入了线性化误差.文中给出了一个基于线性系统模型的位置估计方法.用一个高维的状态向量表示机器人的位置空间,并选用环境路标的全局信息作为观测向量,此时系统动态模型和系统观测模型都是线性的,从而直接运用最优的线性卡尔曼滤波技术进行移动机器人位置估计.这种方法免除了非线性方程的线性近似过程,避免了线性化误差.实验表明,位置估计过程是收敛的、一致的.     

Kinematic parameter calibration of a car-like mobile robot to improve odometry accuracy

Odometry provides fundamental pose estimates for wheeled vehicles. For accurate and reliable pose estimation, systematic and nonsystematic errors of odometry should be reduced. In this paper, we focus on systematic error sources of a car-like mobile robot (CLMR) and we suggest a novel calibration method. Kinematic parameters of the CLMR can be successfully calibrated by only a couple of test driving. After reducing deterministic errors by calibration, odometry accuracy can be further improved by redundant odometry fusion with the extended Kalman filter (EKF). Odometry fusion reduces nonsystematic or stochastic errors. Experimental verifications are carried out using a radio-controlled miniature car.     

空基多平台对空中机动目标的被动跟踪定位算法

针对空中机动目标的被动定位跟踪问题,提出了一种先用静态估计理论对空中目标进行最小二乘估计,再采用基于“当前”统计模型的自适应滤波算法进行滤波处理的方法,取得了比最小二乘估计与卡尔曼滤波相结合的算法更好的效果。仿真结果表明,在跟踪非机动目标时,该算法和最小二乘估计与卡尔曼滤波结合的办法相当;在跟踪机动目标时,该算法的误差明显小于原算法。     

A Nonlinear Filtering Approach for Robust Multi-GNSS RTK Positioning in Presence of Multipath and Ionospheric Delays

In this paper, we develop a new approach of precise positioning using three carrier phase multi-Global Navigation Satellite System (GNSS) measurements in presence of multipath and ionospheric delays. We propose a new nonlinear filter to estimate the user position as well as all the unknown parameters including the integer ambiguities and the ionospheric errors. First, we use a kernel representation of the conditional density and apply a local linearization which yields a Kalman-like correction enhancing the particle filter correction. This new particle Kalman filter approach, is designed to be efficient for the non-Gaussian state and nonlinear measurements model, reduces the number of needed particles, and reduces the risk of divergence. The proposed procedure for multifrequency ambiguity resolution is based on four steps: 1) at each epoch, we compute the float solution adaptively to the dynamic environment by minimizing the noise level and estimating the ionospheric errors using the proposed robust Bayesian particle Kalman filter (RobPKF); 2) we introduce a new carrier phase multipath indicator and use it to derive a related constraint to reject integers candidates that are affected by multipath errors; 3) we apply the LAMBDA method to search the integer ambiguities; and finally 4) validate the fixed solution using a statistical test. We show in this work that the efficient integration of multifrequency/multisystem carriers provides more redundancy in the measurements and better observability for multipath and ionospheric errors estimation for long-baseline RTK positioning. A major advantage of this method is that it is independent of frequencies choice and therefore can be applied for any multi-GNSS measurements (e.g., Global Positioning System (GPS), Galileo, and their combination). Real-time and postprocessing test results show the effectiveness of the developed overall real-time kinematic (RTK) software.     

基于时空混合图卷积网络的机器人定位误差预测及补偿方法

工业机器人作为智能制造的重要载体,在大范围复杂任务中具有巨大潜力。但是,定位精度低且难以控制的问题阻碍了机器人在高精度任务的进一步推广。为了提升机器人作业精度,该文提出一种基于时空混合图卷积网络的机器人定位误差预测及补偿方法。首先通过设计图关系编码模块、时空混合特征解码模块,构建基于图卷积网络的机器人位姿误差预测模型;然后,针对传统迭代补偿方法中机器人逆解次数多导致效率低的问题,该文将定位误差补偿问题转化为优化问题,并利用遗传算法同时对位置和姿态进行误差补偿;最后,通过拉丁超立方抽样方法获得训练集,实现机器人定位误差预测模型的训练,并通过实验验证了定位误差预测的准确性以及补偿的效果。     

基于自适应卡尔曼滤波的侧滑移动机器人运动模型估计

精确实时在线的运动模型对于侧滑移动机器人的运动控制和轨迹规划至关重要,相比于离线模型估计,该文在基于速度瞬心(ICRs)的侧滑移动机器人运动学模型基础上,采用扩展卡尔曼滤波(EKF),在同一特定地形下在线准确得到ICRs的参数值;并针对不同的地形情况,采用k-近邻法对地形进行分类,实时判别机器人当前运行的路面,采用自适应的卡尔曼滤波器(AKF)调整滤波器参数。仿真和实验对比表明,该方法在同一地形和变化地形下均能快速估计出侧滑移动机器人的运动学模型,收敛时间均为3 s以内,可以满足实际使用的需要。