An active hybrid parallel robot for minimally invasive surgery

During the last years, there has been an increase in research in the field of medical robots. This trend motivated the development of a new robotics field called “robotic-assisted minimally invasive surgery”. The paper presents the kinematic and dynamic behavior of a parallel hybrid surgical robot PARASURG-9M. The robot consists of two subsystems: a surgical robotic arm, PARASURG 5M with five motors, and an active robotized surgical instrument PARASIM with four motors. The methodology for the robot kinematics is presented and the algorithm for robot workspace generation is described. PARASURG-9M inverse dynamic simulation is performed using MSC Adams and finally some numerical and simulation results of the developed experimental model with its system control are also described.     

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.     

MBS Approach to Generate Equations of Motions for HiL-Simulations in Vehicle Dynamics

Numerical simulation of system dynamics is today a standard in the design of cars and trucks. The multibody system (MBS) analysis is applied to suspension kinematics and compliant kinematics, handling performance and ride comfort as well as to the generation of load data for lifetime prediction. These simulation tasks are carried out as an off-line simulation. Over the last couple of years the MBS method has been established in the real-time simulation domain, typically for the design of vehicle control systems and the test of electronic control units, using Hardware-in-the-Loop (HiL) simulation. This paper shows how the transition from offline multi-body-system (MBS) models to real-time simulation can be achieved automatically by using a component oriented reduction procedure for vehicle suspensions, which keeps the full component parameterization. This necessitates a transformation of the equations of motion from differential algebraic equations (DAE) into ordinary differential equations (ODE). For use in HiL-simulators the equations of motion with open interfaces of the applied forces and torques are provided to be integrated in a modular dynamic ride model. The work has been carried out during the employment of the author at INTEC GmbH, Argelsrieder Feld 13; 82234 Wessling, Germany; e-mail: intec@simpack.de     

An instrumental variable approach for rigid industrial robots identification

This paper deals with the important topic of rigid industrial robots identification. The usual identification method is based on the use of the inverse dynamic model and the least-squares technique. In order to obtain good results, a well-tuned derivative bandpass filtering of joint positions is needed to calculate the joint velocities and accelerations. However, we can doubt whether the bandpass filter is well-tuned or not. Another approach is the instrumental variable (IV) method which is robust to data filtering and which is statistically optimal. In this paper, an IV approach relevant for identification of rigid industrial robots is introduced. The set of instruments is the inverse dynamic model built from simulated data which are calculated from the simulation of the direct dynamic model. The simulation assumes the same reference trajectories and the same control structure for both the actual and the simulated robot and is based on the previous IV estimates. Furthermore, to obtain a rapid convergence, the gains of the simulated controller are updated according to IV estimates. Thus, the proposed approach validates the inverse and direct dynamic models simultaneously and is not sensitive to initial conditions. The experimental results obtained with a 2 degrees of freedom (DOF) planar prototype and with a 6 DOF industrial robot show the effectiveness of our approach: it is possible to identify 60 parameters in 3 iterations and in 11 s.     

Weakly and fully coupled methods for structural optimization of flexible mechanisms

The paper concerns a detailed comparison between two optimization methods that are used to perform the structural optimization of flexible components within a multibody system (MBS) simulation. The dynamic analysis of flexible MBS is based on a nonlinear finite element formulation. The first method is a weakly coupled method, which reformulates the dynamic response optimization problem in a two-level approach. First, a rigid or flexible MBS simulation is performed, and second, each component is optimized independently using a quasi-static approach in which a series of equivalent static load (ESL) cases obtained from the MBS simulation are applied to the respective components. The second method, the fully coupled method, performs the dynamic response optimization using the time response obtained directly from the flexible MBS simulation. Here, an original procedure is proposed to evaluate the ESL from a nonlinear finite element simulation, contrasting with the floating reference frame formulation exploited in the standard ESL method. Several numerical examples are provided to support our position. It is shown that the fully coupled method is more general and accommodates all types of constraints at the price of a more complex optimization process.     

An offset error compensation method for improving ANN accuracy when used for position control of precision machinery

Artificial Neural Networks (ANNs) have recently become the focus of considerable attention in many disciplines, including robot control, where they can be used as a general class of nonlinear models to solve highly nonlinear control problems. Feedforward neural networks have been widely applied for modelling and control purposes. One of the ANN applications in robot control is for the solution of the inverse kinematic problem, which is important in path planning of robot manipulators. This paper proposes an iterative approach and an offset error compensation method to improve the accuracy of the inverse kinematic solutions by using an ANN and a forward kinematic model of a robot. The offset error compensation method offers potential to generate accurately the inverse solution for a class of problems which have an easily obtained forward model and a complicated solution.Now Lecturing in Taiwan.     

基于RBF神经网络逆系统的机械手解耦控制策略

针对机械手系统具有非线性时变、多变量、强耦合的特点,提出一种基于RBF神经网络逆系统的机械手解耦控制策略。首先证明了系统的可逆性,进一步通过神经网络在线逆辨识建立机械手的神经网络逆系统模型,并将辨识得到的逆模型作为控制器模型与机械手系统串联,构成伪线性复合系统,实现了将具有强耦合特性的多变量输入/输出机械手系统解耦成单个独立的伪线性对象。最后以两关节机械手为仿真对象进行了仿真,仿真结果验证了本方案的有效性和可行性。     

Degeneracy conditions of the dynamic model of parallel robots

Despite their well-known advantages in terms of higher intrinsic rigidity, larger payload-to-weight ratio, and higher velocity and acceleration capacities, parallel robots have drawbacks. Among them, the most important one is surely the presence of singularities in the workspace, which divide the workspace into different aspects (each aspect corresponding to one or more assembly modes) and near which the performance is considerably reduced.In order to increase the reachable workspace of parallel robots, a promising solution consists in the definition of optimal trajectories passing through the singularities to change either the leg working modes or the robot assembly modes. Previous works on the field have shown that it is possible to define optimal trajectories that allow the passing through the robot type 2 singularities. Such trajectories must respect a physical criterion that can be obtained through the analysis of the degeneracy conditions of the parallel robot inverse dynamic model.However, the mentioned works were not complete: they lacked a degeneracy condition of the parallel robot inverse dynamic model, which is not due to type 2 singularity anymore, but to a serial singularity. Crossing a serial singularity is appealing as in that case we can change the robot leg working mode and then potentially access to other workspace zones. This absence is due to the fact that the authors used a reduced dynamic model, which was not taking into account all link dynamic parameters.The present paper aims to fill this gap by providing a complete study of the degeneracy conditions of the parallel robot dynamic model and by demonstrating that it is possible to cross the type 2, but also serial singularity, by defining trajectories that respect some given criteria obtained from the analysis of the degeneracy of the robot dynamic model. It also aims to demonstrate that the serial singularities have impacts on the robot effort transmission, which is a point that is usually bypassed in the literature. All theoretical developments are validated through simulations and experiments.     

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.      

Stable adaptive controller design of robotic manipulators via neuro‐fuzzy dynamic inversion

In this paper, a stable adaptive control approach is developed for the trajectory tracking of a robotic manipulator via neuro‐fuzzy (NF) dynamic inversion, an inverse model constructed by the dynamic neuro‐fuzzy (DNF) model with desired dynamics. The robot neuro‐fuzzy model is initially built in the Takagi‐Sugeno (TS) fuzzy framework with both structure and parameters identified through input/output (I/O) data from the robot control process, and then employed to dynamically approximate the whole robot dynamics rather than its nonlinear components as is done by static neural networks (NNs) through parameter learning algorithm. Since the NF dynamic inversion comprises a cluster of reference trajectories connecting the initial state to the desired state of the robot, the dynamic performance in the initial control stage of robot trajectory tracking can be guaranteed by choosing the optimum reference trajectory. Furthermore, the assumption that the robot states should be on a compact set can be excluded by NF dynamic inversion design. The system stability and the convergence of tracking errors are guaranteed by Lyapunov stability theory, and the learning algorithm for the DNF system is obtained thereby. Finally, the viability and effectiveness of the proposed control approach are illustrated through comparing with the dynamic NN (DNN) based control approach. © 2005 Wiley Periodicals, Inc.     

仿人机器人复杂动作设计中人体运动数据提取及分析方法

提出了仿人机器人复杂动作设计中人体运动数据提取及分析方法. 首先, 通过运动捕捉系统获取人体运动数据, 并采用运动重定向技术, 输出人--机简化模型的数据; 然后, 对运动数据进行分析和运动学解算, 给出基于人体运动数据的仿人机器人逆运动学求解方法, 得到仿人机器人模型的关节角数据; 再经过运动学约束和稳定性调节后, 生成能够应用于仿人机器人的运动轨迹. 最终, 通过在仿人机器人BHR-2上进行刀术实验验证了该方法的有效性.     

Visual Motor Control of a 7 DOF Robot Manipulator Using Function Decomposition and Sub-Clustering in Configuration Space

This paper deals with real-time implementation of visual-motor control of a 7 degree of freedom (DOF) robot manipulator using self-organized map (SOM) based learning approach. The robot manipulator considered here is a 7 DOF PowerCube manipulator from Amtec Robotics. The primary objective is to reach a target point in the task space using only a single step movement from any arbitrary initial configuration of the robot manipulator. A new clustering algorithm using Kohonen SOM lattice has been proposed that maintains the fidelity of training data. Two different approaches have been proposed to find an inverse kinematic solution without using any orientation feedback. In the first approach, the inverse Jacobian matrices are learnt from the training data using function decomposition. It is shown that function decomposition leads to significant improvement in accuracy of inverse kinematic solution. In the second approach, a concept called sub-clustering in configuration space is suggested to provide multiple solutions for the inverse kinematic problem. Redundancy is resolved at position level using several criteria. A redundant manipulator is dexterous owing to the availability of multiple configurations for a given end-effector position. However, existing visual motor coordination schemes provide only one inverse kinematic solution for every target position even when the manipulator is kinematically redundant. Thus, the second approach provides a learning architecture that can capture redundancy from the training data. The training data are generated using explicit kinematic model of the combined robot manipulator and camera configuration. The training is carried out off-line and the trained network is used on-line to compute the joint angle vector to reach a target position in a single step only. The accuracy attained is better than the current state of art.     

人工神经网络在机械手动力学辨识和位置控制中的应用

本文提出了用人工神经网络近似机械手的逆动力学模型,实现基于模型的非线性控制方案,并以实际的两臂液压机械手为对象给出了仿真结果,整个方案的实现不需要任何关于系统模型的知识.仿真结果表明所研究的位置控制系统具有良好的跟踪性能,并且展示了人工神经网络解决非线性系统辨识和控制问题的潜力.     

Real-time humanoid whole-body remote control framework for imitating human motion based on kinematic mapping and motion constraints

In this paper, we propose a whole-body remote control framework that enables a robot to imitate human motion efficiently. The framework is divided into kinematic mapping and quadratic programming based whole-body inverse kinematics. In the kinematic mapping, the human motion obtained through a data acquisition device is transformed into a reference motion that is suitable for the robot to follow. To address differences in the kinematic configuration and dynamic properties of the robot and human, quadratic programming is used to calculate the joint angles of the robot considering self-collision, joint limits, and dynamic stability. To address dynamic stability, we use constraints based on the divergent component of motion and zero moment point in the linear inverted pendulum model. Simulation using Choreonoid and a locomotion experiment using the HUBO2+ demonstrate the performance of the proposed framework. The proposed framework has the potential to reduce the preview time or offline task computation time found in previous approaches and hence improve the similarity of human and robot motion while maintaining stability.     

Velocity control of wheeled mobile robots using computed torque control and its performance for a differentially driven robot

This article presents the development of a Computed Torque Control (CTC) scheme for Cartesian velocity control of Wheeled Mobile Robots (WMRs). The literature presents extensive study on the need and suitability of the CTC scheme for robot arms. Many researchers have identified the benefits of using a CTC scheme for mobile robots. There is however very little information on CTC schemes for controlling mobile robots. The need for the CTC scheme stems from the fact that mobile robots (industrial AGVs) employing conventional velocity control schemes experience side slip due to suspended loads while negotiating curves, and the controller gains and accelerations need to be modified for changes in the dynamics. The structure of the proposed control scheme can be employed to control any mobile robot for which an inverse dynamic model exists, as a CTC scheme requires an inverse dynamic model to compute unique values for the motor current for a given trajectory. It is demonstrated that the existence of the inverse dynamic model is guaranteed for all differentially driven WMRs for all operating conditions, and is not affected by the number of castor wheels in the WMR. In the proposed CTC scheme, the linear and angular velocities of the WMR are controlled by adjusting the WMR accelerations, which are computed based on the motor torques required to follow a given trajectory. The motor torque is pre-computed based on a dynamic model of the mobile robotic system. The simulation and experimental results presented for a differentially driven WMR demonstrate that a computed-torque control scheme provides adaptive cruising and steering control for nominally tuned controller gains compared to a conventional velocity controller to achieve proper road following in the presence of changes in the dynamics. © 1997 John Wiley & Sons, Inc.     

人工多细胞模型的构建及实验

鉴于多细胞组织具有行为多样性以及自适应生长能力,在已有的细胞模拟工作的基础上,提出了可以用于信息处理的人工多细胞模型。该模型以模拟环境和多细胞动态分布式系统作为模型的核心,细胞间联系借助于化学物质的反应扩散过程。结合遗传算法,应用人工多细胞模型设计了自主机器人的导航控制器。在机器人导航控制的仿真实验中获得了多种控制结构的具有避碰、漫游能力的机器人,实验结果验证了模型的可行性、有效性。     

一种柔性前臂机械手控制算法*

本文研究柔性机器人的轨迹跟踪控制问题，通过坐标变换导出以可测关节角为变量的柔性机器人的动态模型，在Ｄｅｌｕｃａ等人提出的ＣＴＪ控制算法基础上提出一种改进的计算力矩控制算法ＩＣＴＪ，并从理论上证明了新算法的收敛性，仿真结果表明ＩＣＴＪ算法比ＣＴＪ算法在跟踪精度有明显的改善。     

A STEP-compliant Industrial Robot Data Model for robot off-line programming systems

Recently, various robot off-line programming systems have promoted their own robot data models, resulting in a plethora of robot representation methods and unchangeable data files among CAx and robot off-line programming systems. The current paper represents a STEP-compliant Industrial Robot Data Model (IRDM) for data exchange between CAx systems and robot off-line programming systems. Using this novel representation method, most resources involved in a robot manufacturing system can be represented. The geometric and mathematic aspects of industrial robots have been defined in IRDM, so that the robot off-line programming system could have abundant information to represent robots’ kinematic and dynamic behaviors. In order to validate the proposed models and approaches, a prototype robot off-line programming system with 3D virtual environment is presented. The functionalities of IRDM not only have significant meaning for providing a unified data platform for robot simulation systems, but also have the potential capability to represent robot language using the object-oriented concept.     

Dynamics analysis of a 3-RRP spherical parallel manipulator using the natural orthogonal complement

In the present research, application of the Natural Orthogonal Complement (NOC) for the dynamic analysis of a spherical parallel manipulator, referred to as SST, is presented. Both inverse and direct dynamics are considered. The NOC and the SST fully parallel robot are explained. To drive the NOC for the SST manipulator, constraints between joint variables are written using the transformation matrices obtained from three different branches of the robot. The Newton–Euler formulation is used to model the dynamics of each individual body, including moving platform and legs of the manipulator. D’Alembert’s principle is applied and Newton–Euler dynamical equations free from non-working generalized constraint forces are obtained. Finally two examples, one for direct and one for inverse dynamics are presented. The correctness and accuracy of the obtained solution are verified by comparing with the solution of the virtual work method as well as commercial multi-body dynamics software.