Transducer and base compliance alter the in situ 6 dof force measured from muscle during an isometric contraction in a multi-joint limb.

Although musculoskeletal models are commonly used, validating the muscle actions predicted by such models is often difficult. In situ isometric measurements are a possible solution. The base of the skeleton is immobilized and the endpoint of the limb is rigidly attached to a 6-axis force transducer. Individual muscles are stimulated and the resulting forces and moments recorded. Such analyses generally assume idealized conditions. In this study we have developed an analysis taking into account the compliances due to imperfect fixation of the skeleton, imperfect attachment of the force transducer, and extra degrees of freedom (dof) in the joints that sometimes become necessary in fixed end contractions. We use simulations of the rat hindlimb to illustrate the consequences of such compliances. We show that when the limb is overconstrained, i.e., when there are fewer dof within the limb than are restrained by the skeletal fixation, the compliances of the skeletal fixation and of the transducer attachment can significantly affect measured forces and moments. When the limb dofs and restrained dofs are matched, however, the measured forces and moments are independent of these compliances. We also show that this framework can be used to model limb dofs, so that rather than simply omitting dofs in which a limb does not move (e.g., abduction at the knee), the limited motion of the limb in these dofs can be more realistically modeled as a very low compliance. Finally, we discuss the practical implications of these results to experimental measurements of muscle actions.

Bayesian parameter identification for vibro-acoustic models with nonparametric uncertainty

Vibration and acoustic analysis at higher frequencies faces two challenges: computing the response without using an excessive number of degrees of freedom, and quantifying its uncertainty due to small spatial variations in geometry, material properties and boundary conditions. Efficient models make use of the observation that when the response of a decoupled vibro-acoustic subsystem is sufficiently sensitive to uncertainty in such spatial variations, the local statistics of its natural frequencies and mode shapes saturate to universal probability distributions. This holds irrespective of the causes that underly these spatial variations and thus leads to a nonparametric description of uncertainty. This work deals with the identification of uncertain parameters in such models by using experimental data. One of the difficulties is that both experimental errors and modeling errors, due to the nonparametric uncertainty that is inherent to the model type, are present. This is tackled by employing a Bayesian inference strategy. The prior probability distribution of the uncertain parameters is constructed using the maximum entropy principle. The likelihood function that is subsequently computed takes the experimental information, the experimental errors and the modeling errors into account. The posterior probability distribution, which is computed with the Markov Chain Monte Carlo method, provides a full uncertainty quantification of the identified parameters, and indicates how well their uncertainty is reduced, with respect to the prior information, by the experimental data. © 2013 Taylor & Francis Group, London.

Avoiding pathologies in very deep networks

Choosing appropriate architectures and regularization strategies of deep networks is crucial to good predictive performance. To shed light on this problem, we analyze the analogous problem of constructing useful priors on compositions of functions. Specifically, we study the deep Gaussian process, a type of infinitely-wide, deep neural network. We show that in standard architectures, the representational capacity of the network tends to capture fewer degrees of freedom as the number of layers increases, retaining only a single degree of freedom in the limit. We propose an alternate network architecture which does not suffer from this pathology. We also examine deep covariance functions, obtained by composing infinitely many feature transforms. Lastly, we characterize the class of models obtained by performing dropout on Gaussian processes.

TAILORED DFB LASER PROPERTIES BY INDIVIDUALLY CHIRPED GRATINGS USING BENT WAVE-GUIDES

DFB lasers with continuously and arbitrarily chirped gratings of ultrahigh spatial precision are implemented by a method we proposed recently, using bent waveguides on homogeneous grating fields. Choosing individual bending functions we generate special chirping functions and obtain additional degrees of freedom to tailor and improve specific device performances, We present two applications for lasers showing several improved device properties and the effectiveness of our method, First, we implement continuously distributed phase-shifted lasers, revealing a considerably reduced photon pile-up, higher single-longitudinal mode stability, higher output power, lower linewidth, and higher yield than conventional abruptly phase-shifted lasers, Second, a novel tuning principle is applied in chirped multiple-section DFB lasers, showing 5.5-nm wavelength tuning, without any gaps, maintaining high side-mode suppression.

运动解耦机理分析与解耦关节设计

分析了运动解耦机理,提出了只有结构解耦才能方便地做到运动解耦,给出了结构解耦的条件:所有回转轴线交于一点,前面的转动使得有关回转轴线的位姿发生变化,后面的转动按照已经发生变化的回转轴线转动,即保证杆的长度不发生变化和杆的回转中心不发生平移。在此基础上,设计了一种三自由度运动解耦液压伺服关节。

冗余度机器人作业区域轨迹的自动规划

机器人化是提高工程机械施工控制自动化的关键问题。对于工程机械机器人 ,特别是具有空间冗余度的工程机械机器人 ,其执行机构末端的轨迹规划的自动实现研究对提高其自动化程度有着重要意义。本文以泵车为例 ,提出了一种泵车布料机构的浇筑过程的自动轨迹规划算法 ,该算法通过将其所浇筑区域离散成浇筑点集 ,对两浇筑点的轨迹利用冗余度机器人学的最小关节范数法 ,并通过在关节速度和加速度非连续之处采用平滑或连续处理 ,从而实现了泵车布料机构浇筑过程的自动轨迹规划。

基于四自由度并联机构的数控机床研究

介绍一种基于混合型四自由度并联平台机构开发的五坐标并联机床 .由于其独特的机构设计 ,与基于 Stewart平台的并联机床相比 ,X方向的进给运动与运动平台分离 ,改由工作台单独进给 ,因而其工作空间成倍增大 .采用龙门框架结构和滚珠丝杠支承方案使机床获得更高的刚度 .给出了该机床运动学逆解 ,控制系统采用基于 PC的数控系统进行五轴联动控制

自治水下机器人全自由度仿真

由于自治水下机器人技术的复杂性 ,系统仿真技术变得越来越重要。系统地分析了自治水下机器人 (AUV ,AutonomousUnderwaterVehicle)的运动模型和空间运动方程 ,运用MATLAB下的SIMULINK ,设计了自治水下机器人的全自由度仿真工具箱 ,包括机器人本体运动、位姿求解和坐标系转换等多个部分 ,可以方便地进行控制方法的全自由度的仿真。

基于运动速度的机器人学习控制

为了使机器人跟踪给定的期望轨线，提出了一种新的基于机器人运动重复性的学习控制法．在这种方法中机器人通过重复试验得到期望运动，这种控制法的优点：一是对于在期望运动附近非线性机器人动力学的近似表达式的线性时变机械系统产生期望运动的输入力矩可不由估计机器人动力学的物理参数形成；二是可以适当的选择位置、速度和加速度反馈增益矩阵，从而加快误差收敛速度；三是加入了加速度反馈，减少了速度反馈，减少了重复试验的次数．这是因为在每次试验的初始时刻不存在位置和速度误差，但存在加速度误差．另外，这种控制法的有效性通过ＰＵＭＡ５６２机器人的前三个关节的计算机仿真结果得到验证。

一种并联激光焊接机器人的机构设计与运动学分析

提出了一种3分支5自由度的并联激光焊接机器人,通过3个分支共同作用,使整机具备了5个自由度的空间加工能力.针对激光焊接,通过分析该机器人的结构特性,建立了其正反解运动学模型,通过解析法求解该模型并进行了计算仿真.最后,对机器人进行激光拼焊实验,仿真数据和实验结果表明,本文研究的并联机器人机构适用于实际的高速、高精度激光焊接。

地震力对高架桥梁上轨道车辆的动态分析与安全性研究

The stability and derailment behavior analysis of railway vehicle system has been discussed by many papers in the past. In stability, give first place to consider hunting behavior of vehicle, therefore most of papers was only consider lateral and yaw motion, but vertical motion is the important factor in derailment behavior, and it will be quite effect in stability. We will probe the running stability and derailment behavior of railway vehicle moving on the viaduct in this paper. In this paper, we use Nadal’s formula to get the derailment quotient. In this paper, the railway vehicle is considered to be three subsystems, carbody, bogie and wheelset. There are secondary suspension systems between carbody and bogies, and primary suspension systems connecting bogies and wheelsets. A vehicle with vertical, lateral, roll, and yaw directions motion is considered to derive the mathematical equations. A vehicle with three-dimensional model has 16 degrees of freedom is used to develop the equations of train motion. In this study, results show that the track shift force and derailment factor increase with an increase of ground acceleration. But for the track shift force and derailment factor, the effects of track irregularities and train speed are very small. Key words: earthquake, railway vehicle, viaduct, derailment factor.

Dexterous Robotic Hands: Kinematics and Control

This report presents issues relating to the kinematics and control of dexterous robotic hands using the Utah-MIT hand as an illustrative example. The emphasis throughout is on the actual implementation and testing of the theoretical concepts presented. The kinematics of such hands is interesting and complicated owing to the large number of degrees of freedom involved. The implementation of position and force control algorithms on such tendon driven hands has previously suffered from inefficient formulations and a lack of sophisticated computer hardware. Both these problems are addressed in this report. A multiprocessor architecture has been built with high performance microcomputers on which real-time algorithms can be efficiently implemented. A large software library has also been built to facilitate flexible software development on this architecture. The position and force control algorithms described herein have been implemented and tested on this hardware.

Quantum Monte Carlo in the Interaction Representation --- Application to a Spin-Peierls Model

A quantum Monte Carlo algorithm is constructed starting from the standard perturbation expansion in the interaction representation. The resulting configuration space is strongly related to that of the Stochastic Series Expansion (SSE) method, which is based on a direct power series expansion of exp(-beta*H). Sampling procedures previously developed for the SSE method can therefore be used also in the interaction representation formulation. The new method is first tested on the S=1/2 Heisenberg chain. Then, as an application to a model of great current interest, a Heisenberg chain including phonon degrees of freedom is studied. Einstein phonons are coupled to the spins via a linear modulation of the nearest-neighbor exchange. The simulation algorithm is implemented in the phonon occupation number basis, without Hilbert space truncations, and is exact. Results are presented for the magnetic properties of the system in a wide temperature regime, including the T-->0 limit where the chain undergoes a spin-Peierls transition. Some aspects of the phonon dynamics are also discussed. The results suggest that the effects of dynamic phonons in spin-Peierls compounds such as GeCuO3 and NaV2O5 must be included in order to obtain a correct quantitative description of their magnetic properties, both above and below the dimerization temperature.

Tracking Human Body Pose on a Learned Smooth Space

Particle filtering is a popular method used in systems for tracking human body pose in video. One key difficulty in using particle filtering is caused by the curse of dimensionality: generally a very large number of particles is required to adequately approximate the underlying pose distribution in a high-dimensional state space. Although the number of degrees of freedom in the human body is quite large, in reality, the subset of allowable configurations in state space is generally restricted by human biomechanics, and the trajectories in this allowable subspace tend to be smooth. Therefore, a framework is proposed to learn a low-dimensional representation of the high-dimensional human poses state space. This mapping can be learned using a Gaussian Process Latent Variable Model (GPLVM) framework. One important advantage of the GPLVM framework is that both the mapping to, and mapping from the embedded space are smooth; this facilitates sampling in the low-dimensional space, and samples generated in the low-dimensional embedded space are easily mapped back into the original highdimensional space. Moreover, human body poses that are similar in the original space tend to be mapped close to each other in the embedded space; this property can be exploited when sampling in the embedded space. The proposed framework is tested in tracking 2D human body pose using a Scaled Prismatic Model. Experiments on real life video sequences demonstrate the strength of the approach. In comparison with the Multiple Hypothesis Tracking and the standard Condensation algorithm, the proposed algorithm is able to maintain tracking reliably throughout the long test sequences. It also handles singularity and self occlusion robustly.

A Neural Network Model for Cursive Script Production

This article describes a neural network model, called the VITEWRITE model, for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a. hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The proposed controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in a given synergy is achieved. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. The separate "score" of onset times used in most prior models is hereby replaced by a self-scaling activity-released "motor program" that uses few memory resources, enables each synergy to exhibit a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless. connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data concerning band movements, such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.