Bayesian network based general correspondence retrieval method for depth sensing with single-shot structured light

This study reinvestigated one of the most fundamental problems in structure light depth sensing field: correspondence retrieval of features between patterns and images. We formulate the global optimum correspondence retrieval by maximizing a conditional probability of correspondence given observed features, which is depicted by a Bayesian network. Different from traditional “code-only” based correspondence retrieval methods, the proposed Bayesian network based method exploits the positional correlations of correspondences of neighboring features, namely, the correspondences of poorly detected features are estimated with the aid of the correspondences of well detected features. The method performs especially well on challenging scenes with rich depth variations, abrupt depth changes, edges, etc. Experiments show that the proposed method increase the correspondence accuracy by about 40% on challenging scenes, compared with traditional “code-only” based correspondence retrieval methods.     

A novel vision-based method for 3D profile extraction of wire harness in robotized assembly process

Automating stages for deformable objects in the production line, in which assembling a wire harness into a predefined position is a complex task owing to the specialized characteristics of the objects. Besides a few automatized systems proposed in the other studies to implement this task under simplified setup conditions, a significant portion of this process remains to be completed manually in industrial environments. To construct an automatic wire harness assembly system, the development of a method that can automatically detect the wire harness profile in a 3D environment and, consequently, guide robot arms to implement assembly tasks is indispensable. Therefore, this study presents an approach that satisfies this requirement, which not only proposes a deep learning-based system to detect the wire profile, but also improves the accuracy of the detected results through a correction method according to the depth values of contiguous areas. The verification of the approach in a robot system that highlights its usefulness and practicality demonstrates the potential of the proposed method to replace people and consequently, reduce labour costs in factory environments.     

基于计算机视觉的油封尺寸检测系统设计

针对油封的大批量、多品种、流水线作业的生产特点以及"零废品率"的检测要求,利用机器视觉检测和计算机图象处理技术,研究开发了适应大批量生产和在线实时检测的油封尺寸机器视觉检测系统;建立了系统软硬件结构,分析了系统的组成和工作原理及工作过程,并对图像处理算法进行了设计;系统标定后的实验检测结果证明,该检测系统能实现产品100%检测的生产目标,具有非接触、在线实时、精度适中、成本低等特点,具有较好的生产通用性。     

以ARM为核心的嵌入式体感遥控器设计

介绍一种以ARM为核心的嵌入式服务机器人体感遥控器的设计。硬件上,本遥控器采用具有ARM Cortex—M3内核的STM32F103C8T6作为核心处理器,选用ST公司的iNEMO惯性导航模块进行手部姿态的识别,同时还具有LCD显示模块、无线收发模块和电源模块;软件上,采用嵌入式操作系统μC／OS—Ⅱ实现多任务的调度和外围设备的管理。经实验验证,本遥控器具有高稳定性、高实时性、高可靠性、低误码率等优点。     

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.     

基于压缩感知的无线传感器网络的数据编码

近年来,基于压缩感知的无线传感器网络数据编码的研究取得了一定的进展,但大部分研究是基于"单跳"的传输模型或者没有考虑网络内节点之间的合作。本文提出了一种基于压缩感知和分簇的传感器网络数据编码方法。首先,节点根据自己的编号伪随机的产生一个M维的列向量,把感知的数据xi投影到此向量上,然后把自己的编号和投影的数据一同传输给簇头。簇头把收到的数据进行求和,并且把计算后的结果传送给下一个簇头,直到sink节点。通过仿真实验和理论分析,验证了本文提出的方法比传统的方法能更好地减少网络内数据传输量。     

激光焊接对接拼缝测量的微景深方法

对于复杂微细、错边量小的激光焊接拼缝测量,现有的视觉传感器方法不能稳定、可靠地提取出拼缝的中心位置、拼缝宽度等信息。为此,提出了基于微景深的激光焊接对接拼缝测量方法,该方法采用高倍光学放大镜头、工业CCD(Charge Coupled Device)/CMOS(Complementary Metal-Oxide-Semiconductor Transistor)相机和LED(Light-Emitting Diode)外部照明装置,以光学成像光轴与被测表面法矢呈45°的方式,组成的对接拼缝的非接触测量系统,既解决了显微放大时景深很小、测量范围小的问题,又解决了狭窄拼缝高光学放大倍数拼缝检测的难题。实验结果表明,该系统可快速准确地提取出拼缝中心坐标、拼缝宽度以及拼缝局部表面法矢。拼缝宽度测量精度优于5 μm,拼缝中心测量精度优于6 μm,可识别的最小拼缝宽度为0.02 mm,完全满足激光焊接对接狭窄拼缝的检测与自动跟踪的要求。     

触觉式机器人焊缝跟踪方法

对机器人在焊缝自动跟踪方面进行了研究，介绍了机器人触觉的力传感器结构和使用方法。对力信号进行解耦及信号解算，有效地消除了机器人在工作过程中振动，抖动对力信号的污染，使机器人有能力自动识别，跟踪焊缝及焊枪位置修正，从而保证焊接质量。     

面向车辆高速移动的频谱感知算法研究

为了解决多普勒效应对于车联网中频谱检测速度与精度的影响,研究了在车辆高速移动情况下的频谱感知算法。首先通过引入认知无线电技术以及车联网技术,构建认知车联网网络,针对频谱利用率低下的问题提出一种频谱感知算法。实验结果表明,该算法可以很好的消除多普勒频移效应,提高了频谱检测的性能,解决了车辆在高速移动状态下信号失真等问题。