Motion in human and machine: A virtual fatigue approach

Achieving human-like behavior of a robot is a key issue of the paper. Redundancy in the inverse kinematics problem is resolved using a biological analogue. It is shown that by means of "virtual fatigue" functions, it is possible to generate robot movements similar to movements of a human arm subject to muscle fatigue. Analytic method enabling control of robot motions in a human-like fashion is described. An example of an anthropomorphic robot arm performing a screw-driving task illustrates the method.     

Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning

This paper proposes a new approach for solving the problem of obstacle avoidance during manipulation tasks performed by redundant manipulators. The developed solution is based on a double neural network that uses Q-learning reinforcement technique. Q-learning has been applied in robotics for attaining obstacle free navigation or computing path planning problems. Most studies solve inverse kinematics and obstacle avoidance problems using variations of the classical Jacobian matrix approach, or by minimizing redundancy resolution of manipulators operating in known environments. Researchers who tried to use neural networks for solving inverse kinematics often dealt with only one obstacle present in the working field. This paper focuses on calculating inverse kinematics and obstacle avoidance for complex unknown environments, with multiple obstacles in the working field. Q-learning is used together with neural networks in order to plan and execute arm movements at each time instant. The algorithm developed for general redundant kinematic link chains has been tested on the particular case of PowerCube manipulator. Before implementing the solution on the real robot, the simulation was integrated in an immersive virtual environment for better movement analysis and safer testing. The study results show that the proposed approach has a good average speed and a satisfying target reaching success rate.     

A Heuristic Approach to the Inverse Differential Kinematics Problem

Inversion of the kinematics of manipulators is one of the central problems in the field of robot arm control. The iterative use of inverse differential kinematics is a popular method of solving this task. Normally the solution of the problem requires a complex mathematical apparatus. It involves methods for solving equation systems as well as algorithms for optimization. In this paper we introduce a naïve heuristic method which works without the need for complex mathematical algorithms. This method forms a simple basis for the more sophisticated control procedures of our robot manipulator (JANUS).     

Recursive modelling in dynamics of Agile Wrist spherical parallel robot

Recursive matrix relations for kinematics and dynamics of the 3-RRR Agile Wrist spherical parallel robot are established in this paper. The prototype of the robot is a three-degrees-of-freedom mechanism with three identical legs. Controlled by concurrent torques, which are generated by some electric motors, the active elements of the robot have three independent rotations. Knowing the rotation motion of the moving platform, we develop first the inverse kinematical problem and determine the velocities and accelerations. Further, the principle of virtual work is used in the inverse dynamic problem. Matrix equations offer iterative expressions and graphs for the power requirement comparison of each of three actuators in two computational complexities: complete dynamic model and simplified dynamic model.     

A New Geometric Subproblem to Extend Solvability of Inverse Kinematics Based on Screw Theory for 6R Robot Manipulators

Geometric inverse kinematics procedures that divide the whole problem into several subproblems with known solutions, and make use of screw motion operators have been developed in the past for 6R robot manipulators. These geometric procedures are widely used because the solutions of the subproblems are geometrically meaningful and numerically stable. Nonetheless, the existing subproblems limit the types of 6R robot structural configurations for which the inverse kinematics can be solved. This work presents the solution of a novel geometric subproblem that solves the joint angles of a general anthropomorphic arm. Using this new subproblem, an inverse kinematics procedure is derived which is applicable to a wider range of 6R robot manipulators. The inverse kinematics of a closed curve were carried out, in both simulations and experiments, to validate computational cost and realizability of the proposed approach. Multiple 6R robot manipulators with different structural configurations were used to validate the generality of the method. The results are compared with those of other methods in the screw theory framework. The obtained results show that our approach is the most general and the most efficient.

     

3-RRRT并联机器人位置正向求解研究

研究一种3-RRRT型并联机器人机构的运动学正向求解方法。根据3-RRRT型并联机器人机构特点以及关节运动的取值范围,提出了以并联机器人支链中支杆的方向余弦和动平台绝对位置坐标为系统的广义坐标的方法,并详细地推导了3-RRRT型并联机器人运动学模型,通过进一步消除中间变量的方法最终获得了易于正、逆运动学求解的只包含3个驱动关节坐标与动平台3个绝对位置坐标的约束方程组。最后,运用基于Moore—Penwse广义逆的牛顿迭代格式编制了MATLAB运动学正向求解程序,并进行了运动学正向求解数值仿真,结果表明求解程序快速有效。     

Active learning of inverse models with intrinsically motivated goal exploration in robots

We introduce the Self-Adaptive Goal Generation Robust Intelligent Adaptive Curiosity (SAGG-RIAC) architecture as an intrinsically motivated goal exploration mechanism which allows active learning of inverse models in high-dimensional redundant robots. This allows a robot to efficiently and actively learn distributions of parameterized motor skills/policies that solve a corresponding distribution of parameterized tasks/goals. The architecture makes the robot sample actively novel parameterized tasks in the task space, based on a measure of competence progress, each of which triggers low-level goal-directed learning of the motor policy parameters that allow to solve it. For both learning and generalization, the system leverages regression techniques which allow to infer the motor policy parameters corresponding to a given novel parameterized task, and based on the previously learnt correspondences between policy and task parameters.We present experiments with high-dimensional continuous sensorimotor spaces in three different robotic setups: (1) learning the inverse kinematics in a highly-redundant robotic arm, (2) learning omnidirectional locomotion with motor primitives in a quadruped robot, and (3) an arm learning to control a fishing rod with a flexible wire. We show that (1) exploration in the task space can be a lot faster than exploration in the actuator space for learning inverse models in redundant robots; (2) selecting goals maximizing competence progress creates developmental trajectories driving the robot to progressively focus on tasks of increasing complexity and is statistically significantly more efficient than selecting tasks randomly, as well as more efficient than different standard active motor babbling methods; (3) this architecture allows the robot to actively discover which parts of its task space it can learn to reach and which part it cannot.     

约束条件下的巡线机器人逆运动学求解

高压输电线路巡检机器人是一种多关节悬挂运动机构,要实现其运动控制就需要根据机器人的本身机构特点和悬挂系统的运动约束条件进行运动学分析.本文利用微分扭转法对巡线机器人的正向运动学进行了求解,并通过对机器人悬挂系统的力学分析,得到了机器人运动学的约束条件,并在这种约束条件下,采用了一种可用来进行实时控制的迭代循环坐标下降(CCD)算法,来进行机器人的逆运动学求解.这种迭代算法对于有运动约束系统的逆运动学求解具有较强的适用性,而且它具有较好的收敛性和有效性,适合于在线计算,便于巡线机器人的实时运动控制.     

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.     

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.     

航天飞机操作器的运动学研究

本文考虑了航天飞机与操作器之间的耦合运动,建立了航天飞机机器人的运动学方程,并提供了一种新的迭代计算法.使用该方法可方便地认操作器所握住的负载的位置和姿态求解操作器各关节的位移,从而顺利地解决了被认为较困难的运动学逆问题.     

Cartesian control of robots without dynamic model and observer design

Most control algorithms for rigid robots are given in joint coordinates. However, since the task to be accomplished is expressed in Cartesian coordinates, inverse kinematics has to be computed in order to implement the control law. Alternatively, one can develop the necessary theory directly in workspace coordinates. This has the disadvantage of a more complex robot model. In this paper, a control-observer scheme is given to achieve exact Cartesian tracking without the knowledge of the manipulator dynamics nor computing inverse kinematics. Also, only joint measurements are used.     

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．     

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.     

Inverse dynamics of the 3-PRR planar parallel robot

Recursive modelling for the kinematics and dynamics of the known 3-PRR planar parallel robot is established in this paper. Three identical planar legs connecting to the moving platform are located in a vertical plane. Knowing the motion of the platform, we develop first the inverse kinematics and determine the positions, velocities and accelerations of the robot. Further, the principle of virtual work is used in the inverse dynamics problem. Several matrix equations offer iterative expressions and graphs for the power requirement comparison of each of three actuators in two different actuation schemes: prismatic actuators and revolute actuators. For the same evolution of the moving platform in the vertical plane, the power distribution upon the three actuators depends on the actuating configuration, but the total power absorbed by the set of three actuators is the same, at any instant, for both driving systems. The study of the dynamics of the parallel mechanisms is done mainly to solve successfully the control of the motion of such robotic systems.     

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.     

Learning Control of Robot Manipulators in Task Space

Two important properties of industrial tasks performed by robot manipulators, namely, periodicity (i.e., repetitive nature) of the task and the need for the task to be performed by the end‐effector, motivated this work. Not being able to utilize the robot manipulator dynamics due to uncertainties complicated the control design. In a seemingly novel departure from the existing works in the literature, the tracking problem is formulated in the task space and the control input torque is aimed to decrease the task space tracking error directly without making use of inverse kinematics at the position level. A repetitive learning controller is designed which “learns” the overall uncertainties in the robot manipulator dynamics. The stability of the closed‐loop system and asymptotic end‐effector tracking of a periodic desired trajectory are guaranteed via Lyapunov based analysis methods. Experiments performed on an in‐house developed robot manipulator are presented to illustrate the performance and viability of the proposed controller.     

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。     

Semantic grasping: planning task-specific stable robotic grasps

We present an example-based planning framework to generate semantic grasps, stable grasps that are functionally suitable for specific object manipulation tasks. We propose to use partial object geometry, tactile contacts, and hand kinematic data as proxies to encode task-related constraints, which we call semantic constraints. We introduce a semantic affordance map, which relates local geometry to a set of predefined semantic grasps that are appropriate to different tasks. Using this map, the pose of a robot hand with respect to the object can be estimated so that the hand is adjusted to achieve the ideal approach direction required by a particular task. A grasp planner is then used to search along this approach direction and generate a set of final grasps which have appropriate stability, tactile contacts, and hand kinematics. We show experiments planning semantic grasps on everyday objects and applying these grasps with a physical robot.     

Integer inverse kinematics method using Fuzzy logic

In this paper, we propose an integer inverse kinematics method for multijoint robot control. The method reduces computational overheads and leads to the development of a simple control system as the use of fuzzy logic enables linguistic modeling of the joint angle. A small humanoid robot is used to confirm via experiment that the method produces the same cycling movements in the robot as those in a human. In addition, we achieve fast information sharing by implementing the all-integer control algorithm in a low-cost, low-power microprocessor. Moreover, we evaluate the ability of this method for trajectory generation and confirm that target trajectories are reproduced well. The computational results of the general inverse kinematics model are compared to those of the integer inverse kinematics model and similar outputs are demonstrated. We show that the integer inverse kinematics model simplifies the control process.