机器人化月表采样器控制系统研究

在未来的深空探测任务中迫切需要采样任务的机器人化。为突破这一关键的技术,我们研制了一台六自由度的机器人化月表采样器原理样机,为其选择合理的控制方式并搭建了一套基于CAN总线的分布式控制系统。本文将详细介绍该机器人化月表采样器的工作原理及其控制系统的基本结构,并将阐述在操作采样器执行采样任务时采用的控制方式。最后搭建试验平台进行采样试验,验证采样器和控制系统的基本功能及其有效性。

平面自由运动半物理仿真系统的初始条件实现

提出了一种平面自由运动半物理仿真系统的设计,对系统仿真试验的初始条件形成过程进行了具体阐述。系统中应用了一种平面两自由度直角坐标运动装置,用于完成系统驱动及速度和位置控制,同时这种运动装置能够进行直角坐标跟随运动,并与其末端测量机构共同实现对平面自由运动物体的高精度大范围位姿测量。建立了系统的运动学模型,并设计相应的控制算法实现试验所需的运动过程,采用半闭环和闭环相结合的方法有效控制系统的末端累积误差;针对提出的复合测量方法,建立了测量原理的数学模型,并进行了精度分析和仿真计算。实验证明这种设计和相应的控制测量方法合理可行。

一种五自由度机电测控系统的设计与实现

文章提出了一种基于五自由度机电系统的测控系统设计方法,并从机械系统构成、测控系统结构及软硬件设计方面论述了系统实现技术。针对系统特定的功能要求,文章详细介绍了位姿组合测量和各位姿自由度控制的测控方法,并应用模块化设计和数据流分析方法进行软硬件设计。通过实验进行系统特性分析,得到运行参数指标。实验证明这种设计方法和实现技术合理可行。

一种新型四自由度并联机器人

提出一种用铅垂导轨上 4个滑块作为原动件的新型四自由度并联机器人 .该并联机器人的动平台能够实现两个方向的移动以及绕两个方向轴线的转动 .研究了该并联机器人的运动学建模方法 ,给出了运动学正、逆解 ,用 Grassmann几何法分析了该并联机器人在其工作空间内不会出现奇异形位 .基于该四自由度并联机器人可以非常方便地开发具有大工作空间的五轴联动数控机床

一种四滑块驱动的并联机构及其运动学建模

提出了一种用水平导轨上 4个滑块作为原动件的 4自由度并联平台机构 ,该机构的动平台能够实现两个方向的移动以及绕两个方向轴线的转动 ,同时研究了该机构的运动学建模方法 ,给出了运动学正、逆解 ,并阐述了其应用前景。

基于模糊推理水下作业系统运动控制研究

水下作业系统是运动学冗余系统,本文将模糊推理方法融入基于任务优先运动学控制算法,对系统载体与机械手进行协调运动分配,同时对系统多个任务进行优化。通过带有3自由度水下机械手的水下作业系统进行算例仿真研究,说明运动控制算法的有效性。

水下机器人的神经网络自适应控制

研究了水下机器人神经网络直接自适应控制方法,采用Lyapunov稳定性理论,证明了存在有界外界干扰和有界神经网络逼近误差条件下,水下机器人控制系统的跟踪误差一致稳定有界.为了进一步验证该水控制方法的正确性和稳定性,利用水下机器人实验平台进行了动力定位实验、单自由度跟踪实验和水平面跟踪实验等验证实验.

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。

A Binocular, Foveated Active Vision System

This report documents the design and implementation of a binocular, foveated active vision system as part of the Cog project at the MIT Artificial Intelligence Laboratory. The active vision system features a three degree of freedom mechanical platform that supports four color cameras, a motion control system, and a parallel network of digital signal processors for image processing. To demonstrate the capabilities of the system, we present results from four sample visual-motor tasks.

Automated Assembly Using Feature Localization

Automated assembly of mechanical devices is studies by researching methods of operating assembly equipment in a variable manner; that is, systems which may be configured to perform many different assembly operations are studied. The general parts assembly operation involves the removal of alignment errors within some tolerance and without damaging the parts. Two methods for eliminating alignment errors are discussed: a priori suppression and measurement and removal. Both methods are studied with the more novel measurement and removal technique being studied in greater detail. During the study of this technique, a fast and accurate six degree-of-freedom position sensor based on a light-stripe vision technique was developed. Specifications for the sensor were derived from an assembly-system error analysis. Studies on extracting accurate information from the sensor by optimally reducing redundant information, filtering quantization noise, and careful calibration procedures were performed. Prototype assembly systems for both error elimination techniques were implemented and used to assemble several products. The assembly system based on the a priori suppression technique uses a number of mechanical assembly tools and software systems which extend the capabilities of industrial robots. The need for the tools was determined through an assembly task analysis of several consumer and automotive products. The assembly system based on the measurement and removal technique used the six degree-of-freedom position sensor to measure part misalignments. Robot commands for aligning the parts were automatically calculated based on the sensor data and executed.

Fuzzy interpolative reasoning via scale and move transformation

Z. Huang and Q. Shen. Fuzzy interpolative reasoning via scale and move transformation. IEEE Transactions on Fuzzy Systems, 14(2):340-359.

Fuzzy interpolation with generalized representative values

Z. Huang and Q. Shen. Fuzzy interpolation with generalized representative values. Proceedings of the 2004 UK Workshop on Computational Intelligence, pages 161-171.

Example-Based Image Restoration via Boosted Classifiers

We propose a novel image registration framework which uses classifiers trained from examples of aligned images to achieve registration. Our approach is designed to register images of medical data where the physical condition of the patient has changed significantly and image intensities are drastically different. We use two boosted classifiers for each degree of freedom of image transformation. These two classifiers can both identify when two images are correctly aligned and provide an efficient means of moving towards correct registration for misaligned images. The classifiers capture local alignment information using multi-pixel comparisons and can therefore achieve correct alignments where approaches like correlation and mutual-information which rely on only pixel-to-pixel comparisons fail. We test our approach using images from CT scans acquired in a study of acute respiratory distress syndrome. We show significant increase in registration accuracy in comparison to an approach using mutual information.

A Network for Learning Kinematics with Application to Human Reaching Models

A model for self-organization of the coordinate transformations required for spatial reaching is presented. During a motor babbling phase, a mapping from spatial coordinate directions to joint motion directions is learned. After learning, the model is able to produce straight-line spatial velocity trajectories with characteristic bell-shaped spatial velocity profiles, as observed in human reaches. Simulation results are presented for transverse plane reaching using a two degree-of-freedom arm.

Bridge-vehicle interaction for structural health monitoring: potentials, applications, and limitations

Structural Health Monitoring (SHM) is an integral part of infrastructure maintenance and management systems due to socio-economic, safety and security reasons. The behaviour of a structure under vibration depends on structure characteristics. The change of structure characteristics may suggest the change in system behaviour due to the presence of damage(s) within. Therefore the consistent, output signal guided, and system dependable markers would be convenient tool for the online monitoring, the maintenance, rehabilitation strategies, and optimized decision making policies as required by the engineers, owners, managers, and the users from both safety and serviceability aspects. SHM has a very significant advantage over traditional investigations where tangible and intangible costs of a very high degree are often incurred due to the disruption of service. Additionally, SHM through bridge-vehicle interaction opens up opportunities for continuous tracking of the condition of the structure. Research in this area is still in initial stage and is extremely promising. This PhD focuses on using bridge-vehicle interaction response for SHM of damaged or deteriorating bridges to monitor or assess them under operating conditions. In the present study, a number of damage detection markers have been investigated and proposed in order to identify the existence, location, and the extent of an open crack in the structure. The theoretical and experimental investigation has been conducted on Single Degree of Freedom linear system, simply supported beams. The novel Delay Vector Variance (DVV) methodology has been employed for characterization of structural behaviour by time-domain response analysis. Also, the analysis of responses of actual bridges using DVV method has been for the first time employed for this kind of investigation.