可重构六维加速度传感器的构型奇异分析

针对六维加速度传感器在工作过程中其弹性体结构可能处于奇异位形的问题,以可重构六维加速度传感器的四种构型为例,分析了构型奇异与基座加速度之间的映射关系。首先,通过建立正向动力学方程和正向运动学方程,构建了基座加速度与质量块相对位姿之间的函数关系。接着,基于Gosselin奇异分析法,推导了弹性体结构的雅可比矩阵,并绘制了奇异曲面散点图。进一步地,分析了质量块的质量与奇异曲面之间的关系。结果表明,12-4、12-6构型可适用较大的加速度范围,而9-3、9-4构型可适用的加速度范围相对较小。研究结论及方法为六维加速度传感器的构型选择及参数优化提供了理论基础。     

FGA优化RBF网络的机器人逆运动学求解研究

针对如何提高六自由度机器人逆运动学的求解精度问题,采用FGA对RBF神经网络的节点中心向量、基宽向量以及网络隐含层到输出层的权向量进行优化,并将其应用于六自由度机器人的逆运动学求解。以机器人工作空间的位姿矩阵作为预测网络的输入变量,以关节空间中的关节角度作为输出变量,构建机器人逆解RBF预测网络,然后选取样本对网络进行训练。最后对网络进行测试,仿真结果显示,优化后的网络预测精度高,泛化能力强。     

六自由度机械臂逆运动学算法

根据D-H参数法确定六自由度机械臂的运动学方程,结合平面几何法和欧拉角变换法将机械臂的逆运动学求解问题分为两部分,一通过平面几何法确定机械臂腕部点的坐标与前三个关节角的关系,二通过欧拉角变换法确定机械臂末端姿态与后三个关节角的关系,根据逆运动解的选取原则从八组解中选取最优解;利用MATLAB中的Robotics Toolbox建立机械臂的正运动学模型,通过多组位姿下的正逆运动解对比验证逆运动学求解算法的准确性;利用VC++中的QueryPerformanceCounter函数和MATLAB中tic-toc语句得到不同算法所消耗的平均时间,通过消耗时间的对比说明该算法的快速性;利用VC++编程实现机械臂写字的过程,通过对比输入字的形状与机械臂末端的实际运动轨迹,进一步验证该算法是一种快速而准确的逆运动学求解算法。     

六自由度运动平台运动学建模与仿真分析

针对并联结构形式的六自由度运动平台,建立了其数学描述方法。通过引入点的复合运动分析理论,建立了平台的完整运动学关系。采用牛顿迭代法进行平台位姿的正解求解,并在此基础上利用解析方法解决了平台速度、加速度正解问题。根据六自由度运动平台的使用特点,提出了一种合理的牛顿迭代初值选择方法。仿真结果表明其迭代寻优次数不超过5次,寻优时间不超过2 ms。分析了计算运动平台包线的理论难度,提出了一种合理的可行方案,经过64次计算,耗时55 ms即完成了平台包线的解算。所建立的运动学模型已应用于某运动平台产品,并可延伸应用于其他并联结构机器人运动分析、并联机床等应用领域。     

基于模糊滑模的六足蛇形臂机器人的末端位姿控制

针对六足蛇形臂机器人的超关节极限和位形偏移量大、末端位姿的控制稳定性不好的问题,提出一种基于模糊滑模的六足蛇形臂机器人的末端位姿控制算法。在超冗余运动学逆解空间中建立蛇形臂机器人的运动学模型,采用修正的DH参数法进行六足蛇形臂机器人的末端位姿参数调节和融合处理,建立蛇形臂机器人的末端位姿力学控制模型,在末端跟随运动中采用外环滑模导纳控制方法进行末端位姿的自适应参数调节,采用滑模误差反馈调节方法确定六足蛇形臂机器人的末端位姿,实现六足蛇形臂机器人准确的姿态定位和参量解算,提高控制稳定性。仿真结果表明:采用该算法进行六足蛇形臂机器人的末端位姿控制的姿态校正性能较好,蛇形臂关节的空间位姿自适应调整能力较强,跟随运动准确,具有很好的位姿控制稳定性。     

高精度解耦六自由度机械臂逆运动学解法

根据6自由度机械臂正交解耦的结构特点,采用位姿分解方式,将6自由度逆运动学降为3自由度位置逆运动学、3自由度方向逆运动学;利用欧儿里德范数导出机械臂定位、定向的逆运动学解析解,使机械臂高速、准确运动.在定向控制方面,提出一种以单位四元数为目标输入的控制形式,只需计算两个角度逆解,既简化计算,又利于实际操作;利用逆运动学计算机械臂的工作空间和奇异点空间,借助移动机器人车体自由度弥补因计算以及关节长度不够引起的奇异位形,极大扩展了机械手臂的有效运动区域.     

仿生六足机器人步态设计与运动仿真

仿生六足机器人稳定性好、灵活度高在众多领域得到了使用。为实现对六足机器人的运动控制和性能分析，通过构建机器人D-H连杆坐标系结合机械结构参数，确定了运动学正解、逆解表达式；设计可修改参数的机器人行走参考步态，引入了控制机体保持水平的姿态控制和适应不平整地形的腿着陆控制。以MATLAB的Simulink作为仿真环境，构建机器人模型，完成运动仿真，并对测量到的运动数据和腿部关节输出力矩进行分析。其结果表明，机器人能够跟随所设计步态生成的轨迹连续稳定行走，使用基于模型设计的方法，验证了所提算法的正确性和可行性，最后得到了腿部各关节力矩分布，为后续六足机器人设计与运动控制提供参考。     

补偿机器人定位误差的神经网络

先进的机器人由计算机执行程序来完成各种作业，靠计算关节变量的函数得到手爪的位姿，这些函数一般不准确，使计算值与实际值有较大误差；重复精度０．１ｍｍ的机器人该误差可能达到１０ｍｍ。已有的机器人运动学误差补偿方法需要分析误差来源，使其参数化，并辨识这些参数，六自由度机器人的这种参数已达７２个之多。本文提出一种机器人运动学误差补偿的神经网络模型，利用改进的误差反传（ＢＰ）学习算法，在ＲＭ－５０１机器人进     

具有双插补避障功能的七关节机械臂控制系统研究

具有更多自由度的七关节机械臂在保证末端运动位姿的前提下,可实现更好的避障轨迹设计,但是给控制系统研发带来很大难题。针对此问题,首先从传统的冗余机械臂运动控制算法出发,建立基于梯度投影法的逆运动学方程;然后,针对其不足,结合六关节机械臂的解析解方法,提出了一种新的既可以避免机械臂腕部碰撞,又可以对机械臂上指定点运动位姿进行监测的双插补算法,实现了具有主动避障与碰撞监测的实时运动控制算法。最后,搭建了七关节机械臂和与之配套的软硬件控制平台,开发了实时的三维仿真软件,并通过实验验证了所提算法的正确性与有效性。     

基于RRT搜索算法的六自由度机械臂避障路径规划

针对三维空间下的机器人路径规划问题,提出了一种改进的快速随机树算法。该算法以机器人的出发点作为随机树的根节点,以基准向量为基准选择节点的扩展方向,逐渐增加叶子节点,降低了节点的随机性,提高了扩展速度。通过运动学反解快速规划出一条全局优化路径,对于反解失败的点,做了规划替代,保证了路径规划的平滑性,降低了机器人工作过程中的抖动,从而得到能够抵达目标位姿的安全路径。     

Comparison of RBF and MLP neural networks to solve inverse kinematic problem for 6R serial robot by a fusion approach

In this paper, a fusion approach to determine inverse kinematics solutions of a six degree of freedom serial robot is proposed. The proposed approach makes use of radial basis function neural network for prediction of incremental joint angles which in turn are transformed into absolute joint angles with the assistance of forward kinematics relations. In this approach, forward kinematics relations of robot are used to obtain the data for training of neural network as well to estimate the deviation of predicted inverse kinematics solution from the desired solution. The effectiveness of the fusion process is shown by comparing the inverse kinematics solutions obtained for an end-effector of industrial robot moving along a specified path with the solutions obtained from conventional neural network approaches as well as iterative technique. The prominent features of the fusion process include the accurate prediction of inverse kinematics solutions with less computational time apart from the generation of training data for neural network with forward kinematics relations of the robot.     

Automatic reconstruction of 3D human motion pose from uncalibrated monocular video sequences based on markerless human motion tracking

We present a method to reconstruct human motion pose from uncalibrated monocular video sequences based on the morphing appearance model matching. The human pose estimation is made by integrated human joint tracking with pose reconstruction in depth-first order. Firstly, the Euler angles of joint are estimated by inverse kinematics based on human skeleton constrain. Then, the coordinates of pixels in the body segments in the scene are determined by forward kinematics, by projecting these pixels in the scene onto the image plane under the assumption of perspective projection to obtain the region of morphing appearance model in the image. Finally, the human motion pose can be reconstructed by histogram matching. The experimental results show that this method can obtain favorable reconstruction results on a number of complex human motion sequences.     

Solution of forward kinematics in Stewart platform using six rotary sensors on joints of three legs

A novel method is proposed for real-time solution of direct kinematics problem of Stewart platform (SP) using six measurements on three legs’ joints consisting of the rotations of three legs in two directions. After the application of the method on a laboratory sample SP, it is observed that the method is preferable to the conventional method that uses the length measurements of all six legs, in the grounds of industrial applicability. It is due to simpler implementation, less expense, easier maintenance, and stress-free assembly. The algorithms of both forward and inverse kinematics are fully derived based on geometric relationships between the platform states and the measurement data. The sensitivity to the measurement errors is analyzed theoretically and is applied through a computer simulation to several configurations of the sample SP which are uniformly distributed in the workspace. The variances of measurement errors for those configurations are compared between the conventional and proposed methods and it is observed that: the proposed method operates more accurate in position measurement especially in lateral movements. Additionally, the proposed method is not too sensitive to direction of movement and geometry of the SP.     

A projected back-tracking line-search for constrained interactive inverse kinematics

Inverse kinematics is the problem of manipulating the pose of an articulated figure in order to achieve a desired goal disregarding inertia and forces. One can approach the problem as a non-linear optimization problem or as non-linear equation solving. The former approach is superior in its generality and ability to generate realistic poses, whereas the latter approach is recognized for its low iteration cost. Therefore, many prefer equation solving over optimization for interactive applications. In this paper we present a projected-gradient method for solving an inverse kinematics problem interactively, which exhibit good performance and precision. The method is compared to existing work in terms of visual quality and accuracy. Our method shows good convergence properties and deals with joint constraints in a simple and elegant manner. Our main contribution lies in an explicit incorporation of joint limits in an interactive solver. This makes it possible to compute the pose in each frame without the discontinuities exhibited by existing key frame animation techniques.     

基于解析法和遗传算法的机械手运动学逆解

研究优化机械手轨迹规划问题,机械手运动时要具有稳定性避障性能。针对平面3自由度冗余机械手优化控制问题,建立机械手的结构模型。提出用解析法和遗传算法相结合满足具有计算量小和适应性强的特点。在给定机械手末端执行器的运动轨迹,按着机械手冗余自由度,运动轨迹上每个点对应的关节角有无穷多个解。而通过算法可以找到一组最优的关节角,可得到优化机械手运动过程中柔顺性和避障点。仿真结果表明,该算法可以快速收敛到全局最优解,可用于计算冗余机械手运动学逆解,并可实现机器人的轨迹规划和避障优化控制。     

Classification and characterization of inverse kinematics solutions for anthropomorphic manipulators

Topological properties of the kinematics map are exploited to develop a novel method for redundancy parameterization and extremely fast inverse kinematics solutions for 7-DOF anthropomorphic manipulators and animation characters. The method consists of generating joint angles vectors (configurations) and determining their associated hand position/orientation (pose) via the known forward kinematics. The generated data are classified into various inverse kinematics solutions manifolds. These manifolds are subsequently segmented so that the redundancy can be parameterized and the solutions can be represented by simple equations whose parameters are stored for rapid online computation. During the online phase, given the desired hand pose, the appropriate stored parameters are retrieved and various inverse kinematics solutions are computed. The online time to provide various solutions is of the order of several microseconds, which allow real-time inverse kinematics evaluations for fast moving animation characters or manipulators.     

改进末端跟随运动的超冗余蛇形臂机器人运动学逆解

针对超冗余蛇形臂机器人运动学逆解中计算量大、超关节极限和位形偏移量大的问题,提出了一种改进末端跟随运动的逆解算法．在末端跟随法中引入蛇形臂弯曲角度的约束,调整关节位置的更新方式,使关节在蛇形臂轴线上运动．通过依次更新关节的空间位置,将超冗余多节蛇形臂的运动学逆解转化为2自由度单节蛇形臂的运动学逆解．仿真分析了蛇形臂机器人在基座移动和基座固定条件下的轨迹跟踪效果,对比了同一目标位置下不同方法的性能．结果表明,改进后的算法能保证蛇形臂的弯曲角度不超过给定范围,关节的运动量从末端到基座依次减小,机器人的运动更协调;与基于雅可比矩阵的数值法和现有启发式方法相比,该方法运算量降低,机器人整体位形偏移量减小,能用于蛇形臂机器人的实时控制．     

机器人逆标定方法研究

在分析传统机器人位姿标定方法的基础上，提出了一种新的机器人标定方法：基于神经网络的逆标定方法。这种标定方法把机器人实际位姿和相应的关节角误差分别作为前馈神经网络的输入和输出来训练网络，从而获得机器人任意位姿时的关节角误差值，通过修改关节值来提高机器人的位姿精度。这种标定方法把所有因素引起的误差均归结为关节角误差，无须求解机器人逆运动学方程，实现了误差的在线补偿。把标定结果与基于运动学模型的参数法的标定结果进行了比较分析。仿真和试验结果均证明了这种方法比传统方法标定效果更好，且更方便简单，避免了其他传统标定方法繁琐的建模及参数辨识过程。     

Neural networks and the inverse kinematics problem

Inverse kinematics is a fundamental problem in robotics. Past solutions for this problem have been realized through the use of various algebraic or algorithmic procedures. In this paper the use of feedforward neural networks to solve the inverse kinematics problem is examined for three different cases. A closed kinematic linkage is used for mapping input joint angles to output joint angles. A three-degree-of-freedom manipulator in 3D space is used to test mappings from both cartesian and spherical coordinates to manipulator joint coordinates. A majority of the results have average errors which fall below 1% of the robot workspace. The accuracy indicates that neural networks are an alternate method for performing the inverse kinematics estimation, thus introducing the fault-tolerant and high-speed advantages of neural networks to the inverse kinematics problem.This paper also shows the use of a new technique which reduces neural network mapping errors with the use of error compensation networks. The results of the work are put in perspective with a survey of current applications of neural networks in robotics.     

基于Octomap的仿人机器人局部环境与能力图模型算法

基于3D点云数据的机器人三维空间能力图模型算法存在体素网格搜索计算量大的问题，由于OcTree在三维空间细分时的层次化优势，提出一种基于Octomap的局部环境与能力图模型算法。首先，根据NAO机器人的关节组成、正向运动学、逆向运动学和刚体坐标变换，对NAO仿人机器人构建全身二叉树状运动学模型；其次在此基础上使用前向运动学在笛卡儿空间计算离散的三维可达点云，并将其作为机器人终端效应器的基础工作空间；然后重点描述将点云空间表示转化为Octomap空间节点表示的方法，尤其是空间节点的概率更新方法；最后提出根据节点几何关系进行空间节点更新顺序选择的优化方法，从而高效地实现了仿人机器人能力图的空间优化表示。实验结果表明，相对于之前的原始Octomap更新方法，优化后的算法能降低近30%空间节点数，提高计算效率。