用于机器人轨迹定位纠正系统的回声状态神经网络研究

循环神经网络(RecurrentNeuralNetworks)是人工神经网络(ArtificialNeuralNetworks)中重要的分支,与前馈神经网络(ForwardNeuralNetworks)相比具有更好的时间序列学习能力。但长期以来其学习法一直不能脱离前馈神经网络而自成一体,回声状态神经网络(EchoStateNetworks(ESN))是打破这一局面的全新学习方法。其独特的结构,良好的短期记忆能力,方便的学习方法,不俗的非线性特性是以前循环神经网络所不可比的。本文在介绍了回声状态神经网络之后将其用于四轮机器人的位置测量系统中,有良好的表现。     

Theory of edge perception for computer vision feedback control

In this paper, the authors address the problem of edge-perception for its applications to vision-feedback control in robotic systems.In natural vision, the recognition of objects takes place through the process consisting of eye system, neural networks and cognition. The cognitive process, in turn yields a phenomenon known as perception. This is the phenomenon of perception of physical attributes, such as edges, color and texture, etc., which is responsible for the recognition of objects through the natural vision processes.In this paper, we make an attempt to postulate the theory of perception for gray-level images. The gray-level images, when going through the cognitive and perception processes, are contaminated by the uncertainty; here we call it cognitive uncertainty.The studies in this paper are confined to the phenomenon of edge-perception for two-dimensional gray-level images, however, these studies can be extended to other types of visual attributes both in two-dimensional and three-dimensional spaces. Indeed, the perception of these attributes, which attempts to emulate the human vision system, may help in the design of a truly robust computer vision-feedback control system for robotic applications.     

基于偏差向量的机器人装配系统零件抓取精度分析

应用偏差向量描述了影响机器人抓取零件过程的各种误差，提出了计算零件抓取成功性方法，在引入手爪的偏差矫正能力的概念后，建立了确定零件抓取精度的模型，该模型适合于各种零件抓取过程，是研究机器人装配系统的装配精度和装配质量的基础。     

斗轮挖掘机斗齿的研制

通过对3100m^3/h斗轮挖掘机斗齿的材料、铸造工艺、热处理工艺及焊接工艺的试验研究，研制出了满足矿山使用要求的新型斗齿，使用性能达到国外同类产品先进水平，完全可以替代进口斗齿。     

CCWELD:A CIM system for robotic arc welding

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An open-source multi-DOF articulated robotic educational platform for autonomous object manipulation

This research presents an autonomous robotic framework for academic, vocational and training purpose. The platform is centred on a 6 Degree Of Freedom (DOF) serial robotic arm. The kinematic and dynamic models of the robot have been derived to facilitate controller design. An on-board camera to scan the arm workspace permits autonomous applications development. The sensory system consists of position feedback from each joint of the robot and a force sensor mounted at the arm gripper. External devices can be interfaced with the platform through digital and analog I/O ports of the robot controller. To enhance the learning outcome for beginners, higher level commands have been provided. Advanced users can tailor the platform by exploiting the open-source custom-developed hardware and software architectures. The efficacy of the proposed platform has been demonstrated by implementing two experiments; autonomous sorting of objects and controller design. The proposed platform finds its potential to teach technical courses (like Robotics, Control, Electronics, Image-processing and Computer vision) and to implement and validate advanced algorithms for object manipulation and grasping, trajectory generation, path planning, etc. It can also be employed in an industrial environment to test various strategies prior to their execution on actual manipulators.     

Evaluation of hydraulic excavator Human–Machine Interface concepts using NASA TLX

This study evaluated newly proposed Human–Machine Interface (HMI) design concepts for improving the ergonomics of hydraulic excavators. The design concepts were based on an augmented interaction technique which involved the use of heads-up display (HUD) and coordinated control as HMI elements. Two alternative HMI designs were elaborated in order to separately evaluate the ergonomic impacts of the head-up display and the coordinated control by comparing them to the standard HMI design. The effectiveness of these three HMI designs in terms of the reduction of the operators' mental and physical workload were assessed by conducting experiments utilizing human subjects, ages 23–35 years. The National Aeronautics and Space Administration's Task Load Index (NASA TLX) method was used for collecting subjective workload scores based on a weighted average of ratings of six factors: Mental Demand, Physical Demand, Temporal Demand, Own Performance, Effort, and Frustration Level. The results showed that the type of HMI design affects different aspects of the operator's workload. Indeed, it showed how the proposed augmented interaction is an effective solution for reducing the ergonomic gaps in terms of mental workload, and to a lesser extent the physical workload, subjected by the standard HMI design.     

Underactuated fingers controlled by robust and adaptive trajectory following methods

Choosing an appropriate control scheme to alleviate nonlinearities and uncertainties is not a trivial task, especially when models are not easily available and practical evaluation provides the only means for actual performance assessment. Various factors can contribute to these nonlinearities and uncertainties, such as friction and stiction. Thus, this article investigates four different control schemes, namely PID, adaptive, conventional sliding mode control (SMC) and integral sliding mode control (ISMC) which are implemented in the Bristol Elumotion Robot Hand (BERUL) to analyse and overcome the aforementioned problems. The hand has five fingers with 16 joints and all fingers are underactuated. The implementation of the proposed control schemes are challenging since the BERUL fingers have significant friction, stiction and unknown parameters. The fingers are light in weight and fragile. Comparative performance characteristics have shown that the ISMC is the most suitable candidate to provide good experimental trajectory following and positioning control for underactuated BERUL fingers.     

并联式混合动力液压挖掘机动力源特性研究

针对液压挖掘机在15s的一个典型工作周期下的工况,分析液压挖掘机的工况特点,建立并联式混合动力液压挖掘机动力源部分的数学模型,提出一种并联式混合动力液压挖掘机动力参数的优化组合配置,研究空间矢量脉宽调制(SVPWM)控制的永磁同步电机（PMSM）以及NiMH电池组在典型工作周期下的发电/电动、充电/发电能量转化效率、电机转速、转矩等动态响应特性以及SOC变化特性.仿真结果表明,该优化组合的动力总成能满足液压挖掘机动力需求和能量转化效率要求,能提升整机系统的油耗特性,并且电池系统只充当能量交互的媒介,实现液压挖掘机的高效节能的控制.     

适用于RPA工具的卷积序列推荐算法

在机器人流程自动化(Robotic Process Automation, RPA)软件中,经常采用序列推荐系统让机器人完成判断、选择等人工处理的任务。然而常用的序列推荐系统受限于序列信息的提取困难等问题,难以得到广泛应用。为了解决这一问题,构建一种基于Inception的卷积序列推荐模型,把时间和潜在空间中的用户行为序列信息嵌入进一幅“图像”中,并通过动态和静态2种不同的卷积层提取其中的局部特征,全面地提取用户的短期兴趣偏好,同时将用户嵌入矩阵作为用户的长期兴趣偏好嵌入到卷积层的输出中,共同构建完整的用户兴趣偏好,提升推荐性能。通过在3种公开数据集MovieLens 1M、Gowalla、Steam上分别进行实验,验证了基于Inception的卷积序列推荐模型的性能优于最新的序列推荐模型,在Top-N序列推荐的3种评价指标中（精确率、召回率、平均AP值）,平均提升幅度在10%左右,单个指标上的最大提升幅度为14%。