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.

     

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.     

物理受限冗余机械臂逆运动学凸优化求解

针对冗余机械臂逆运动学求解结果极有可能超过机械臂物理限制的问题,提出一种基于凸优化的逆运动学求解方法使得逆解结果满足物理约束.首先分析了关节速度与力矩关系,采用机械臂动能及重复运动为优化指标,以关节速度、关节力矩为优化变量.然后将逆运动学求解问题转化为凸优化问题,进一步转化为二次规划问题,充分利用冗余特性,实现逆运动学求解时避免关节位置、关节速度、关节力矩极限.最后利用7自由度冗余机械臂KUKA LBR iiwa进行仿真,求解关节量结果符合物理极限及优化准则.结果表明本文提出的方法适用于物理受限冗余机械臂的逆运动学求解.     

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。     

冗余机器人逆运动学解流形的多目标优化

针对冗余机器人逆运动学插值优化算法运算量大、实时性差的缺点,提出一种基于流形的多目标优化算法.将冗余机器人逆运动学解空间看作一个光滑流形,对位置工作空间流形和姿态工作空间流形分别进行降维分析,然后结合提出的优化目标函数得到冗余机器人相应的优化逆解.在冗余机器人多目标优化中各个优化性能指标很可能是相互对立矛盾的,这就需要根据优先权的高低进行加权设置,以达到冗余机器人解空间的整体优化,得到的优化逆解往往不是单个的解,而是一个优化的解流形.最后利用飞机S形进气道进行逆运动学仿真验证了所用方法的合理性.     

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。     

一种求解冗余机械臂逆运动学的优化方法

基于加权最小范数法,推导出一种避免计算雅可比矩阵伪逆的优化方法.首先对加权雅可比矩阵的6维非奇异子矩阵求逆,得到逆运动学的特解和齐次解.然后用特解减去齐次解沿特解方向的分量得到运动学逆解.通过一个7自由度冗余机械臂的算例和仿真证明了方法在保证求解精度、降低求解难度以及避免关节极限方面的有效性     

仿人机器人轻型高刚性手臂设计及运动学分析

重点研究了7自由度轻型高刚性作业型仿人机器人手臂的机构设计和运动学分析方法.首先使用动力学仿真、有限元分析与实验测试相结合的方法,设计了仿人机器人手臂,该机械臂结构紧凑、质量轻、刚度高.同时,提出结合查询数据库和逆运动学计算去模仿人类手臂姿态,从而获得逆运动学最优解的方法.该方法不仅解决了冗余自由度带来的逆运动学多解问...     

基于ELM-IWO机械臂运动学逆解算法研究

为了提高机械臂控制中运动学逆解的速度和准确度,提出了一种基于入侵性杂草优化的机械臂运动学逆解方法.通过D-H法建立了工业六自由度机械臂的正向运动学模型,并利用训练速度较快的ELM(极限学习机)计算机械臂关节角度向量,即输出其逆运动学初解.利用IWO(入侵性杂草优化)算法对得到的初始逆解进行优化,取最小适应度下的杂草位置作为输出,以便得到最佳的逆运动学求解.实验结果表明,相比基于PSO-BP神经网络的求解方法,基于ELM-IWO算法的机械臂末端执行器的精度更高,实时性更好.     

全局和声搜索方法及其在仿人灵巧臂逆运动学求解中的应用

仿人灵巧臂逆运动学(IK)问题可转化为等效的最小化问题,并采用数值优化方法求解.和声搜索(HS)是模拟乐师在音乐演奏中调整音调现象的一种启发式搜索方法,目前还尚未在机器人机械臂逆运动学问题中得到应用.本文提出一种基于粒子群体智能的全局和声搜索方法(GHSA),该方法在和声搜索算法中引入微粒群操作(PSO),采用粒子群策略替代常规和声搜索算法中的搜索法则创作新和声,通过粒子自身认知和群体知识更新和声变量位置信息平衡算法对解空间全局探索与局部开发间能力;同时算法还引入变异操作增强算法跳出局部最优解能力,基准函数测试表明该方法改善了全局搜索能力及求解可靠性.在此基础上以七自由度(7-DOF)冗余仿人灵巧臂为例,考虑以灵巧臂末端位姿误差和“舒适度”指标构建适应度函数并采用GHSA算法求解其逆运动学(IK)问题,数值仿真结果表明了该方法是解决仿人灵巧臂逆运动学问题的一种有效方法.     

Analytical Inverse Kinematics Solver for Anthropomorphic 7-DOF Redundant Manipulators with Human-Like Configuration Constraints

It is a common belief that service robots shall move in a human-like manner to enable natural and convenient interaction with a human user or collaborator. In particular, this applies to anthropomorphic 7-DOF redundant robot manipulators that have a shoulder-elbow-wrist configuration. On the kinematic level, human-like movement then can be realized by means of selecting a redundancy resolution for the inverse kinematics (IK), which realizes human-like movement through respective nullspace preferences. In this paper, key positions are introduced and defined as Cartesian positions of the manipulator’s elbow and wrist joints. The key positions are used as constraints on the inverse kinematics in addition to orientation constraints at the end-effector, such that the inverse kinematics can be calculated through an efficient analytical scheme and realizes human-like configurations. To obtain suitable key positions, a correspondence method named wrist-elbow-in-line is derived to map key positions of human demonstrations to the real robot for obtaining a valid analytical inverse kinematics solution. A human demonstration tracking experiment is conducted to evaluate the end-effector accuracy and human-likeness of the generated motion for a 7-DOF Kuka-LWR arm. The results are compared to a similar correspondance method that emphasizes only the wrist postion and show that the subtle differences between the two different correspondence methods may lead to significant performance differences. Furthermore, the wrist-elbow-in-line method is validated as more stable in practical application and extended for obstacle avoidance.     

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.

     

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.     

Visual motor control of a 7 DOF robot manipulator using a fuzzy SOM network

A fuzzy self-organizing map (SOM) network is proposed in this paper for visual motor control of a 7 degrees of freedom (DOF) robot manipulator. The inverse kinematic map from the image plane to joint angle space of a redundant manipulator is highly nonlinear and ill-posed in the sense that a typical end-effector position is associated with several joint angle vectors. In the proposed approach, the robot workspace in image plane is discretized into a number of fuzzy regions whose center locations and fuzzy membership values are determined using a Fuzzy C-Mean (FCM) clustering algorithm. SOM network then learns the inverse kinematics by on-line by associating a local linear map for each cluster. A novel learning algorithm has been proposed to make the robot manipulator to reach a target position. Any arbitrary level of accuracy can be achieved with a number of fine movements of the manipulator tip. These fine movements depend on the error between the target position and the current manipulator position. In particular, the fuzzy model is found to be better as compared to Kohonen self-organizing map (KSOM) based learning scheme proposed for visual motor control. Like existing KSOM learning schemes, the proposed scheme leads to a unique inverse kinematic solution even for a redundant manipulator. The proposed algorithms have been successfully implemented in real-time on a 7 DOF PowerCube robot manipulator, and results are found to concur with the theoretical findings.     

Forward and inverse kinematics of a 5-DOF hybrid robot for composite material machining

Hybrid robots consist of both serial and parallel mechanisms, which have advantages in stiffness and workspace compared with serial/parallel robots when machining composite material. However, the forward and inverse kinematics of hybrid robots generally do not have analytic solutions. This paper deals with the analytic forward and inverse kinematics solutions of a 5-degree-of-freedom (DOF) hybrid robot which consists with a 3-DOF 2UPU/SP parallel mechanism (PM) and a 2-DOF rotating head. In the forward kinematic problem, a method is proposed to transfer the high order kinematic equation to a 4th-order polynomial based on the Sylvester's dialytic elimination, and the analytic solutions can be further obtained by Ferrari's method. In the inverse problem, the redundant Euler angles expressed by four rotations are firstly proposed for decoupling different motions, then, the closed-form solution of inverse kinematics can be found. Finally, a simulation trajectory is given, and the result shows that the accuracy of the solutions’ calculation reaches femtometer grade and the efficiency reaches microsecond grade; furthermore, an experiment is performed on the prototype to validate the effectiveness of the proposed forward and inverse kinematics.     

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。     

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．     

Fast and robust numerical method for inverse kinematics with prioritized multiple targets for redundant robots

In this paper, a new numerical method for inverse kinematics with prioritized multiple targets is proposed. The proposed method is constructed based on the virtual spring model and joint-based damping control. The targets are prioritized by adjusting the effect of the virtual springs. The proposed method has the following three features. First, it does not require complex calculations such as a Jacobian matrix projection into the null space. Second, it can solve prioritized inverse kinematics problems in the position level without integrating the joint velocity. Third, it is robust to parameter variations and singular configurations. The second feature is motivated by the background that most industrial robots in factories are used as position-controlled robots. Simulation experiments using a 9-DOF redundant robot show that the proposed method is faster and more robust than the conventional method. The proposed method is expected to be useful for helping to avoid collisions between links and obstacles using the redundancy.