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.     

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.     

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.

     

基于改进天牛须算法的电力攀爬机器人运动学逆解算法

为了提高电力系统的自动化水平,减轻电力工人在检修高压输电系统时的劳动强度,同时保障电力工人人身安全,提出并设计一种可以攀爬电力铁塔的六自由度关节式机器人,针对该构型进行运动学分析和求解.为解决传统的解析法用于机械臂逆运动学求解过程中存在操作繁琐和奇异点无法逆运算等问题,提出一种基于改进天牛须算法的电力攀爬机器人运动学逆解算法.首先,对电力攀爬机器人进行DH建模,得到正运动学方程;然后,使用正运动学方程和目标位姿建立代价函数,采用改进天牛须算法对代价函数优化;最后,使用Matlab实现此算法进行仿真验证.实验结果表明,与传统的天牛须算法、改进遗传算法以及改进粒子群算法相比,所提出算法具有较好的收敛性,求解精度较高.     

一类不满足Pieper准则的机器人逆运动学解析解获取方法

当机器人构型不满足Pieper准则的时候,传统的运动学建模方法通常无法求出机器人逆运动学解析解.针对不满足Pieper准则的机器人构型,基于相邻关节轴换位前后正运动学结果不变的准则,提出一种全新的运动学建模方法——相邻关节轴建立顺序换位法,并给出了适用该方法的机器人构型条件.以一种不满足Pieper准则的微创腹腔手术机器人为例,应用该方法求解出其精确的解析解,并通过Matlab Simulink仿真验证了该方法的正确性.     

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。     

基于Paden-kahan子问题的冗余度机器人运动学求解

针对7DOF机器人的逆运动学求解问题,提出了一种可提高运动控制精度的混合算法.这种算法使用 旋量理论来描述机器人的运动.它首先求出对运动学性能指标进行优化的速度级逆解;然后固定一个特殊关节,将 问题转化为非冗余度机器人的运动控制,应用Paden-Kahan子问题法得到逆运动学封闭解.通过仿真实例,证实了 这种混合算法的有效性.     

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.     

机器人逆运动学求解的可视化算法

机器人逆运动学求解的可视化算法包含两部分，数值求解两个(或一个)非线性方程和4(或5)自由度机器人封闭解，实现了任意结构的6自由度机器人的逆运动学方程的求解，根据D-H参数表生成机器人三维模型实现机器人结构的可视化，有效地判断逆解的合理性，并为机器人学习提供了辅助工具。     

基于回转变换张量的6R机器人运动学研究

在牧野坐标系下建立了六自由度喷涂机器人的运动学模型,采用回转变换张量法求出了该机器人的正逆运动学解析表达式.算法在VS.NET平台上用组件技术实现并仿真,仿真结果验证了算法的正确性和实时性.     

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.     

基于遗传算法的机器人运动学逆解

在分析以往逆解方法的基础上，提出了用遗传算法求解机器人运动学逆解的方法，给出了用于优化求解的适合度函数，并提出用二次编码法提高解的精度．计算机模拟证明：该方法能快速收敛于全局最优解，能给出机器人的可能解，并能计算冗余度机器人的逆解．     

Reliability-based approach to the inverse kinematics solution of robots using Elman's networks

The solution of inverse kinematics problem of redundant manipulators is a fundamental problem in robot control. The inverse kinematics problem in robotics is the determination of joint angles for a desired cartesian position of the end effector. For the solution of this problem, many traditional solutions such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. Furthermore, many neural network approaches have been done to this problem. But the neural network-based solutions are not much reliable due to the error at the end of learning. Therefore, a reliability-based neural network inverse kinematics solution approach has been presented, and applied to a six-degrees of freedom (dof) robot manipulator in this paper. The structure of the proposed method is based on using three networks designed parallel to minimize the error of the whole system. Elman network, which has a profound impact on the learning capability and performance of the network, is chosen and designed according to the proposed solution method. At the end of parallel implementation, the results of each network are evaluated using direct kinematics equations to obtain the network with best result.     

Computer generation of symbolic kinematics for robot manipulators

Computer generation of symbolic solutions for the direct and inverse robot kinematics is a desired capability not previously available to robotics engineers. In this article, we present a methodology for the design of a software system capable of solving the direct and inverse kinematics for n degree of freedom (dof) manipulators in symbolic form. The inputs to the system are the Denavit-Hartenberg parameters of the manipulator. The outputs of the system are the direct and inverse kinematics solutions in symbolic form. The system consists of a symbolic processor to perform matrix and algebraic manipulations and an expert system to solve the class of nonlinear equations involved in the solution of the inverse kinematics problem. The system can be used to study robot kinematics configurations whose inverse kinematics solutions are not known to exist a priori. Two examples are included to illustrate its capabilities. The first example provides explicit analytical solutions, previously believed nonexistent, for a 3 dof manipulator. A second example is included for a robot whose inverse kinematics solution requires intensive algebraic manipulations.     

Trajectory Planning with Collision Avoidance for Redundant Robots Using Jacobian and Artificial Potential Field-based Real-time Inverse Kinematics

This study proposes an algorithm for combining the Jacobian-based numerical approach with a modified potential field to solve real-time inverse kinematics and path planning problems for redundant robots in unknown environments. With an increase in the degree of freedom (DOF) of the manipulator, however, the problems in realtime inverse kinematics become more difficult to solve. Although the analytical and geometrical inverse kinematics approach can obtain the exact solution, it is considerably difficult to solve as the DOF increases, and it necessitates recalculations whenever the robot arm DOF or Denavit-Hartenberg (D-H) parameters change. In contrast, the numerical method, particularly the Jacobian-based numerical method, can easily solve inverse kinematics irrespective of the aforementioned changes including those in the robot shape. The latter method, however, is not employed in path planning for collision avoidance, and it presents real-time calculation problems. This study accordingly proposes the Jacobian-based numerical approach with a modified potential field method that can realize real-time calculations of inverse kinematics and path planning with collision avoidance irrespective of whether the case is redundant or non-redundant. To achieve this goal, the use of a judgment matrix is proposed for obstacle condition identification based on the obstacle boundary definition; an approach for avoiding the local minimum is also proposed. After the obstacle avoidance path is generated, a trajectory plan that follows the path and avoids the obstacle is designed. Finally, the proposed method is evaluated by implementing a motion planning simulation of a 7-DOF manipulator, and an experiment is performed on a 7-DOF real robot.

     

基于运动学正解的三转动并联机构迭代补偿控制

首先建立三转动并联机构的运动学方程,研究该并联机构的运动学反解和运动学正解,然后建立该机 构的动力学方程并分析其动力学特性．基于该机构研制了能复现自行武器行进时的车体姿态变化过程的动态模拟器 试验台．该试验台利用运动学反解算法进行闭环控制,并采用基于运动学正解的开环迭代补偿控制算法修正姿态驱 动信号,使试验台的响应逐渐逼近期望的姿态指令．测试表明该系统时域波形复现精度优于95%,验证了迭代补偿 控制的有效性．     

Position,Jacobian and workspace analysis of a 3-PSP spatial parallel manipulator

This paper investigates the problems of kinematics, Jacobian, singularity and workspace analysis of a spatial type of 3-PSP parallel manipulator. First, structure and motion variables of the robot are addressed. Two operational modes, non-pure translational and coupled mixed-type are considered. Two inverse kinematics solutions, an analytical and a numerical, for the two operational modes are presented. The direct kinematics of the robot is also solved utilizing a new geometrical approach. It is shown, unlike most parallel robots, the direct kinematics problem of this robot has a unique solution. Next, analytical expressions for the velocity and acceleration relations are derived in invariant form. Auxiliary vectors are introduced to eliminate passive velocity and acceleration vectors. The three types of conventional singularities are analyzed. The notion of non-pure rotational and non-pure translational Jacobian matrices is introduced. The non-pure rotational and non-pure translational Jacobian matrices are combined to form the Jacobian of constraint matrix which is then used to obtain the constraint singularity. Finally, two methods, a discretization method and one based on direct kinematics are presented and robot non-pure translation and coupled mixed-type reachable workspaces are obtained. The influence of tool length on workspace is also studied.     

Bionic Quadruped Robot Dynamic Gait Control Strategy Based on Twenty Degrees of Freedom

Quadruped robot dynamic gaits have much more advantages than static gaits on speed and efficiency, however high speed and efficiency calls for more complex mechanical structure and complicated control algorithm. It becomes even more challenging when the robot has more degrees of freedom. As a result, most of the present researches focused on simple robot, while the researches on dynamic gaits for complex robot with more degrees of freedom are relatively limited. The paper is focusing on the dynamic gaits control for complex robot with twenty degrees of freedom for the first time. Firstly, we build a relatively complete 3D model for quadruped robot based on spring loaded inverted pendulum (SLIP) model, analyze the inverse kinematics of the model, plan the trajectory of the swing foot and analyze the hydraulic drive. Secondly, we promote the control algorithm of one-legged to the quadruped robot based on the virtual leg and plan the state variables of pace gait and bound gait. Lastly, we realize the above two kinds of dynamic gaits in ADAMS-MATLAB joint simulation platform which testify the validity of above method.      

A neural-network committee machine approach to the inverse kinematics problem solution of robotic manipulators

In robotics, inverse kinematics problem solution is a fundamental problem in robotics. Many traditional inverse kinematics problem solutions, such as the geometric, iterative, and algebraic approaches, are inadequate for redundant robots. Recently, much attention has been focused on a neural-network-based inverse kinematics problem solution in robotics. However, the result obtained from the neural network requires to be improved for some sensitive tasks. In this paper, a neural-network committee machine (NNCM) was designed to solve the inverse kinematics of a 6-DOF redundant robotic manipulator to improve the precision of the solution. Ten neural networks (NN) were designed to obtain a committee machine to solve the inverse kinematics problem using separately prepared data set since a neural network can give better result than other ones. The data sets for the neural-network training were prepared using prepared simulation software including robot kinematics model. The solution of each neural network was evaluated using direct kinematics equation of the robot to select the best one. As a result, the committee machine implementation increased the performance of the learning.