基于四自由度并联机构的数控机床研究

介绍一种基于混合型四自由度并联平台机构开发的五坐标并联机床 .由于其独特的机构设计 ,与基于 Stewart平台的并联机床相比 ,X方向的进给运动与运动平台分离 ,改由工作台单独进给 ,因而其工作空间成倍增大 .采用龙门框架结构和滚珠丝杠支承方案使机床获得更高的刚度 .给出了该机床运动学逆解 ,控制系统采用基于 PC的数控系统进行五轴联动控制

供排气管对接机械手的机构研究与参数优化

根据水下救援作业中连接供排气管的需求,失事对象救生围壁的结构形式,遵循水下机械手机构选型原则,设计了一种专用的六功能气管对接水下机械手,并从机械手工作空间和轻量化要求两方面建立了机械手的数学模型,对其机构参数进行了优化设计。最后对优化结果进行了分析和工作空间的仿真。结果表明该机械手的设计能满足供排气管对接作业要求,达到了预期目的。

并联Stewart机构位姿误差分析

从并联机构与串联机构的运动学等效 ,并联机构本身特征与并联机构实际工作空间出发 ,考虑各分支末端误差对最终运动平台末端误差的影响 ,提出了并联机构位姿误差放大因子分析法·依据位姿误差放大因子具有对误差定量分析的特点 ,该分析方法既可用于机构参数优化 ,又可用于结构精度设计· 最后 ,给出了一个实例说明本方法的有效性·

基于模糊神经网络的多移动机器人自学习协调系统

研究多移动机器人的运动规划问题．针对机器人模型未知或不精确以及环境的动态变化，提出一种自学习模糊控制器（ＦＬＣ）来进行准确的速度跟踪．首先通过神经网络的学习训练构造ＦＬＣ，再由再励学习算法来在线调节ＦＬＣ的输出，以校正机器人运动状态，实现安全协调避撞

一种三自由度并联机器人机构运动学计算

出一种新型的三杆三自由度并联机器人机构，并推导了其运动学正反计算式，给出运动空间和根据作业空间设计结构参数的计算式。

7自由度机器人的图谱问题

本文在分析、归纳、综合的基础上作出了7自由度机械臂的图谱.包括位置空间、奇异空间、回避障碍和回避奇异等问题.给出了一种普遍适用的方法.并介绍了该方法的使用,对7自由度机器人的结构设计和选型有一定的参考价值.

多关节机器人工作空间的分析与评价方法

本文运用 H-D 变换的基本原理,结合极坐标变换导出了产生n自由度多关节机器人工作空间的递推算法,当给定了机器人的结构尺寸,即可将机器人工作空间在一特定平面内的边界图形用计算机打出并计算出机器人工作空间容积。在本文的另一部分介绍了两种计算机器人工作空间的性能指标,最后用几个机器人的结构参数进行计算和讨论。

一种新型并联机器人工作空间分析

对一种新型并联机器人的工作空间问题作了讨论,基于运动学逆解,通过极限边界数值搜索算法求取了并联机器人工作空间,并通过MATLAB仿真得到了该并联机器人工作空间的实体模型。

Minimizing Residual Vibrations in Flexible Systems

Residual vibrations degrade the performance of many systems. Due to the lightweight and flexible nature of space structures, controlling residual vibrations is especially difficult. Also, systems such as the Space Shuttle remote Manipulator System have frequencies that vary significantly based upon configuration and loading. Recently, a technique of minimizing vibrations in flexible structures by command input shaping was developed. This document presents research completed in developing a simple, closed- form method of calculating input shaping sequences for two-mode systems and a system to adapt the command input shaping technique to known changes in system frequency about the workspace. The new techniques were tested on a three-link, flexible manipulator.

Redundant Sensors for Mobile Robot Navigation

Redundant sensors are needed on a mobile robot so that the accuracy with which it perceives its surroundings can be increased. Sonar and infrared sensors are used here in tandem, each compensating for deficiencies in the other. The robot combines the data from both sensors to build a representation which is more accurate than if either sensor were used alone. Another representation, the curvature primal sketch, is extracted from this perceived workspace and is used as the input to two path planning programs: one based on configuration space and one based on a generalized cone formulation of free space.

Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning

Q. Meng and M. H. Lee, 'Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning', Journal of Intelligent and Robotic Systems, 48(1), pp 97-114, 2007.

Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.

Developmental changes in the response to obstacles during prehension

Adults are proficient at reaching to grasp objects of interest in a cluttered workspace. The issue of concern, obstacle avoidance, was studied in 3 groups of young children aged 11-12, 9-10, and 7-8 years (n = 6 in each) and in 6 adults aged 18-24 years. Adults slowed their movements and decreased their maximum grip aperture when an obstacle was positioned close to a target object (the effect declined as the distance between target and obstacle increased). The children showed the same pattern, but the magnitude of the effect was quite different. In contrast to the adults, the obstacle continued to have a large effect when it was some distance from the target (and provided no physical obstruction to movement).

The effect of obstacle position on reach-to-grasp movements

Numerous everyday tasks require the nervous system to program a prehensile movement towards a target object positioned in a cluttered environment. Adult humans are extremely proficient in avoiding contact with any non-target objects (obstacles) whilst carrying out such movements. A number of recent studies have highlighted the importance of considering the control of reach-to-grasp (prehension) movements in the presence of such obstacles. The current study was constructed with the aim of beginning the task of studying the relative impact on prehension as the position of obstacles is varied within the workspace. The experimental design ensured that the obstacles were positioned within the workspace in locations where they did not interfere physically with the path taken by the hand when no obstacle was present. In all positions, the presence of an obstacle caused the hand to slow down and the maximum grip aperture to decrease. Nonetheless, the effect of the obstacle varied according to its position within the workspace. In the situation where an obstacle was located a small distance to the right of a target object, the obstacle showed a large effect on maximum grip aperture but a relatively small effect on movement time. In contrast, an object positioned in front and to the right of a target object had a large effect on movement speed but a relatively small effect on maximum grip aperture. It was found that the presence of two obstacles caused the system to decrease further the movement speed and maximum grip aperture. The position of the two obstacles dictated the extent to which their presence affected the movement parameters. These results show that the antic ipated likelihood of a collision with potential obstacles affects the planning of movement duration and maximum grip aperture in prehension.

Kinematic Design of a 6-DOF Parallel Manipulator with Decoupled Translation and Rotation

A new three-limb, six-degree-of-freedom (DOF) parallel manipulator (PM), termed a selectively actuated PM (SA-PM), is proposed. The end-effector of the manipulator can produce 3-DOF spherical motion, 3-DOF translation, 3-DOF hybrid motion, or complete 6-DOF spatial motion, depending on the types of the actuation (rotary or linear) chosen for the actuators. The manipulator architecture completely decouples translation and rotation of the end-effector for individual control. The structure synthesis of SA-PM is achieved using the line geometry. Singularity analysis shows that the SA-PM is an isotropic translation PM when all the actuators are in linear mode. Because of the decoupled motion structure, a decomposition method is applied for both the displacement analysis and dimension optimization. With the index of maximal workspace satisfying given global conditioning requirements, the geometrical parameters are optimized. As a result, the translational workspace is a cube, and the orientation workspace is nearly unlimited.