面向冗余机器人实时控制的逆运动学求解有效方法

针对7自由度冗余机器人实时运动控制,对机器人逆运动学提出了一种新的求解方法.采用位姿分解方式,使7自由度冗余机器人逆运动学简化为4自由度位置逆运动学求解.在梯度投影法得到位置优化解的基础上,利用机器人封闭解公式求得一组优化解.通过对7自由度机器人仿真分析,表明了该方法的有效性.     

七自由度带电作业机器人逆运动学求解方法研究

本文以七自由度双臂带电作业机器人为研究对象,针对七自由度逆运动学求解计算复杂,实时控制困难的问题,在分析机器人的机械结构及建立正向运动学模型的基础上,采用位姿分解法与代数迭代法相结合的方式求解运动学逆解,将七自由度逆运动学求解转化为四自由度位置冗余问题,并设计了具体的程序流程图,经过仿真验证,该算法减小了逆运动学求解的计算量,提高了机器人控制的实时性。     

仿人机器人7DOF腿部的运动分析与仿真

传统的6自由度腿部逆运动学求解可以得到唯一解,仿人机器人7自由度腿部由于冗余自由度的存在,其逆运动学求解比6自由度腿部更难.本文采用D-H方法对现有的仿人机器人7自由度的下肢进行运动学建模与分析,用位姿分离法求解步行运动中的逆运动学解,在LMS Virtual.Lab仿真平台上仿真,为解决机器人的动力学问题做必要的准备.     

面向冗余机器人实时控制的逆运动学求解有效方法

针对-自由度冗余机器人实时运动控制,对机器人逆运动学提出了一种新的求解方法.采用位姿分解方式,使-自由度冗余机器人逆运动学简化为,自由度位置逆运动学求解.在梯度投影法得到位置优化解的基础上,利用机器人封闭解公式求得一组优化解.通过对-自由度机器人仿真分析,表明了该方法的有效性.

     

冗余自由度超声检测机器人的逆运动学分析

针对一类冗余自由度超声检测机器人的传统逆运动学求解算法耗时长且准确度低的问题,提出了一种基于集合划分和解析解法相结合的逆运动学求解算法。首先采用De-navit-Hartenberg方法建立检测机器人的运动学方程;其次,利用解析解法求出机器人逆解的解析表达式,并提出三种自由度分配方案;最后,选择合适的自由度分配方案,据此对超声波探头位姿集合作划分,结合逆解解析式求出运动学逆解。实际应用中,借助十一轴超声波检测机器人,利用该算法对具有复杂外形的飞机螺旋桨叶片进行检测。结果表明,与传统的纯数值解法相比,该算法能够快速得到精确的运动学逆解。     

基于神经网络的机器人位姿逆解

本文运用神经网络求解机器人运动学位姿逆解，突破了文献局限于研究位置逆解的状况，首次实现自组织神经网络求解机器人姿态逆解。     

EMR系统机器人运动学和工作空间的分析

本文研究了EMR系统机器人的运动学和工作空间。首先对机器人的工作空间进行了分析,并提出了求解机器人末端灵活性的方法;接着解决了机器人位姿正运动学描述和逆运动学求解的问题;最后针对欠自由度的机器人结构,提出采用一组变量来描述机器人的速度运动学,避免了采用伪逆求逆速度运动学。     

6R机器人实时逆运动学算法研究

提出一套解决各类6R机器人逆运动学问题的实时算法. 一般算法通过矢量计算和16阶矩阵分解得到一般6R机器人的最多16组逆运动学解. 封闭解法直接提取运动学等式求出关节变量的解析解. 组合算法将封闭解法或一般算法的结果作为初始值, 采用牛顿-拉夫森方法迭代出逆运动学精确解, 适用于所有接近满足封闭解条件或一般算法条件的6R机器人. 求解实验结果表明, 整套算法最大算法时间约为2.03 ms, 为任意几何结构的6R机器人应用于强实时系统提供了逆运动学解决方案.     

6自由度喷涂机器人的运动学分析与仿真

运用回转变换张量法建立了6自由度喷涂机器人的运动学模型,并利用消元法简化了运动学逆解的求解过程,得出了较为简易的解析解.利用ADAMS软件建立了机器人的虚拟样机模型,并进行运动仿真分析,不仅证明了运动学模型的正确性,也为后续研究奠定了基础.     

六自由度操作手的逆运动学问题

本文将六自由度操作手分解为位置结构和姿态结构，利用转换矩阵法对两结构进行了详细的逆运动学分析，在此基础上建立了操作手的运动学求逆算法，与一般的迭代算法或优化算法相比较，本算法收敛速度快￣［１］，而且能得到满足约束的全部解，与解析方法相比较，它具有通用性的优点，尤其适用于机器人仿真系统中。     

Effective and efficient feature selection for large-scale data using Bayes’ theorem

This paper proposes one method of feature selection by using Bayes＇ theorem. The purpose of the proposed method is to reduce the computational complexity and increase the classification accuracy of the selected feature subsets. The dependence between two attributes （binary） is determined based on the probabilities of their joint values that contribute to positive and negative classification decisions. If opposing sets of attribute values do not lead to opposing classification decisions （zero probability）, then the two attributes are considered independent of each other, otherwise dependent, and one of them can be removed and thus the number of attributes is reduced. The process must be repeated on all combinations of attributes. The paper also evaluates the approach by comparing it with existing feature selection algorithms over 8 datasets from University of California, Irvine （UCI） machine learning databases. The proposed method shows better results in terms of number of selected features, classification accuracy, and running time than most existing algorithms.     

Spatial-temporal joint probability images for video segmentation

Effective annotation and content-based search for videos in a digital library require a preprocessing step of detecting, locating and classifying scene transitions, i.e., temporal video segmentation. This paper proposes a novel approach—spatial-temporal joint probability image (ST-JPI) analysis for temporal video segmentation. A joint probability image (JPI) is derived from the joint probabilities of intensity values of corresponding points in two images. The ST-JPT, which is a series of JPIs derived from consecutive video frames, presents the evolution of the intensity joint probabilities in a video. The evolution in a ST-JPI during various transitions falls into one of several well-defined linear patterns. Based on the patterns in a ST-JPI, our algorithm detects and classifies video transitions effectively.Our study shows that temporal video segmentation based on ST-JPIs is distinguished from previous methods in the following way: (1) It is effective and relatively robust not only for video cuts but also for gradual transitions; (2) It classifies transitions on the basis of predefined evolution patterns of ST-JPIs during transitions; (3) It is efficient, scalable and suitable for real-time video segmentation. Theoretical analysis and experimental results of our method are presented to illustrate its efficacy and efficiency.     

Finding the likely behaviors of static continuous nonlinear systems

This paper describes two methods for predicting the likely behaviors of static continuous nonlinear systems with varying input values. The methods use a parameterized equation model and upper or lower bounds on the joint input density to bound the likelihood of a behavior, such as a state variable being inside a numeric range. Using a bound on the density instead of the density itself is desirable because the density's parameters and shape are not exactly known. The first method is limited to using lower density bounds. It finds rough bounds at first, and then refines them as more iterations of the method are allowed. The second method is a hit-or-miss version of sample-mean Monte Carlo. Unlike the first method, the second method can also handle upper density bounds, which are more useful than lower density bounds, but the generated probability bounds are only approximate. However, standard deviations on the bounds are given and become small as the sample size increases. In contrast to other researchers' methods, the two methods described here (1) find all the possible system behaviors, and tell how likely they are, (2) do not just approximate the distribution of possible outcomes without some measure of the error magnitude, (3) do not use discretized variable values, which limit the events one can find probability bounds for, (4) can handle density bounds, and (5) can handle such criteria as two state variables both being inside a numeric range.     

The boundaries for joint angles of isocomfort for sitting and standing males based on perceived comfort of static joint postures

This study presents data for the joint angles of isocomfort (JAI) in sitting and standing males based on perceived comfort ratings for static joint postures maintained for 60 s. The JAI value was defined as a boundary indicating joint deviation (an angle) from neutral posture, within which the perceived comfort for different body joint postures is expected to be the same. An experiment for quantifying perceived comfort ratings was conducted using the free modulus method of magnitude estimation. Based on experimental results, regression equations were derived for each joint posture, to represent the relationships between different levels of joint deviation/joint posture and corresponding normalized comfort scores. The JAI values were developed for nine verbal categories of joint comfort. The JAIs with the marginal comfort levels, one of the nine verbal categories used, for most joint postures around the wrist, elbow, neck and ankle were similar to the maximum range of motion (ROM) values for these joints. However, the JAIs with the marginal comfort category for back and hip postures were much smaller than the maximum ROM values for these joints. There were no significant differences in JAI expressed in terms of the percentage of the corresponding maximum ROM values between sitting and standing postures. The relative 'marginal comfort index', defined as the percentage of JAIs for the marginal comfort relative to the corresponding maximum ROM values, for the hip was the smallest among all joints. This was followed, in an increasing order of the marginal comfort index, by the lower back and shoulder, while the marginal comfort index for the elbow joint was the largest. The results of this study suggest that static postures maintained for 60 s cause greater discomfort for the hip joint than for the other joints studied, and less discomfort for the elbow than for the other joints. The data about JAIs can be used as guidelines for enhancing postural comfort when designing a variety of human-machine tasks where static postures cannot be eliminated.     

Information fusion in wireless sensor networks with source correlation

In this paper, we consider a central estimating officer (CEO) scenario, where sensors observe a noisy version of a binary sequence generated by a single source (the “phenomenon”) and the access point (AP)’s goal is to estimate, by properly fusing the received data, this sequence. Due to this system model, the data sent by the sensors are correlated and, therefore, it is possible to exploit a proper a priori information in the localized fusion operation performed at the AP. In the presence of channel coding at the sensors and block faded communication links, we first derive the optimum maximum a priori probability (MAP) joint decoding and fusion rule, showing its computational unfeasibility. We then derive two suboptimal decoding/fusion strategies. In the first case, the fusion rule exploits the source correlation and receives, at its input, the soft-output values generated by a joint channel decoder (JCD). Two possible iterative JCD algorithms are proposed: one with “circular” iterations between the component decoders (associated with the sources) and one with “parallel” iterations between the component decoders. For each algorithm, two information combining strategies are considered. In the second case, a separate channel decoding (SCD) scheme is considered and the correlation is exploited only during the fusion operation. Our results show that the scheme with SCD followed by fusion basically leads to the same probability of decision error of the scheme with JCD and fusion with, however, a much lower computational complexity, thus making it suitable to resource-constrained scenarios.     

A biomechanical shoulder strain index based on stabilizing demand of shoulder joint

Abstract

Work-related shoulder joint disorders contribute considerably to absenteeism in the workplace. To identify the tasks that are stressful to the shoulder joint, a strain index was formulated based on the concept of concavity compression—a shoulder stabilizing mechanism. The magnitude and direction of the shoulder joint reaction forces were used in formulating the strain index. A two phase experiment was conducted. In Phase 1, participants performed 30 different manual handling tasks. The tasks were categorized into low, medium and high strain tasks based on their strain index values. In Phase 2, out of the 30 tasks, repetitive exertions of three tasks (low, medium and high strain index values) were simulated using three external loads (0.91, 1.81 and 2.72?kg). The muscle activity data recorded from eight shoulder muscles showed that tasks with higher strain index values induced significantly greater activation and muscle fatigue than tasks with lower strain index values.Practitioner Summary: The strain index developed in this study is a conclusive estimation of the concavity compression required for shoulder joint stabilization. It can be used to identify the activities that may contribute to the risks of shoulder disorders. Abbreviation BLS Bureau of the Labor Statistics

     

The boundaries for joint angles of isocomfort for sitting and standing males based on perceived comfort of static joint postures

This study presents data for the joint angles of isocomfort (JAI) in sitting and standing males based on perceived comfort ratings for static joint postures maintained for 60 s. The JAI value was defined as a boundary indicating joint deviation (an angle) from neutral posture, within which the perceived comfort for different body joint postures is expected to be the same. An experiment for quantifying perceived comfort ratings was conducted using the free modulus method of magnitude estimation. Based on experimental results, regression equations were derived for each joint posture, to represent the relationships between different levels of joint deviation/joint posture and corresponding normalized comfort scores. The JAI values were developed for nine verbal categories of joint comfort. The JAIs with the marginal comfort levels, one of the nine verbal categories used, for most joint postures around the wrist, elbow, neck and ankle were similar to the maximum range of motion (ROM) values for these joints. However, the JAIs with the marginal comfort category for back and hip postures were much smaller than the maximum ROM values for these joints. There were no significant differences in JAI expressed in terms of the percentage of the corresponding maximum ROM values between sitting and standing postures. The relative ‘marginal comfort index’, defined as the percentage of JAIs for the marginal comfort relative to the corresponding maximum ROM values, for the hip was the smallest among all joints. This was followed, in an increasing order of the marginal comfort index, by the lower back and shoulder, while the marginal comfort index for the elbow joint was the largest. The results of this study suggest that static postures maintained for 60 s cause greater discomfort for the hip joint than for the other joints studied, and less discomfort for the elbow than for the other joints. The data about JAIs can be used as guidelines for enhancing postural comfort when designing a variety of human-machine tasks where static postures cannot be eliminated.     

SLOTSEQ: A FORTRAN IV program for comparing two sequences of observations

The program described has been of use in comparing physical logs from two boreholes when the aim has been to “line up”, or correlate, the boreholes in order to identify sections where the physical logs show similar patterns. This is achieved by slotting the two sequences of measurements into one joint sequence which preserves the stratigraphic ordering within each of the original sequences, and subject to this requirement, insures that similar sets of measurements are placed near to one another in the joint sequence. Various types of constraint also may be imposed on the allowable slottings, for example readings from different logs which occur in a persistent marker bed can be required to be close together in the joint slotting. The method is illustrated on a set of data comprising gamma, sonic, and induction logs from two wells. Detailed input specifications and a listing of the program are given.     

Robust glottal source estimation based on joint source-filter model optimization

This paper describes a robust glottal source estimation method based on a joint source-filter separation technique. In this method, the Liljencrants-Fant (LF) model, which models the glottal flow derivative, is integrated into a time-varying ARX speech production model. These two models are estimated in a joint optimization procedure, in which a Kalman filtering process is embedded for adaptively identifying the vocal tract parameters. Since the formulated joint estimation problem is a multiparameter nonlinear optimization procedure, we separate the optimization procedure into two passes. The first pass initializes the glottal source and vocal tract models by solving a quasi-convex approximate optimization problem. Having robust initial values, the joint estimation procedure determines the accuracy of model estimation implemented with a trust-region descent optimization algorithm. Experiments with synthetic and real voice signals show that the proposed method is a robust glottal source parameter estimation method with a high degree of accuracy.