The use of sensors is not new in this field, and sensors have successfully been used for seam tracking for more than 20 years in robotic arc welding。 Basically, two dif- ferent principles are used, through-arc sensing and op- tical sensors。 Through-arc sensing uses the arc itself and requires a small weaving motion of the weld torch。 Op- tical sensors are often based on  a  scanning  laser light and triangulation to measure the distance to the  weld joint。 Both methods have  some  characteristic  features that make them more suitable in certain situations。 It should  be  noted  that  the  through-arc  sensing technique

is rather inexpensive in comparison with an optical seam tracker。 The principal types of industrial arc-welding sensors that have been employed are optical and arc sensors。 If the arc sensing has been dominant till  the 1980s, the trend nowadays is focused on optical im- provement for intelligent programming as well as intelli- gent sensors。

Many sensors for seam tracking and seam finding are available in the market。 The nature of the work defines the suitability of a particular type  of  sensor。 However, due to an acceptable level of accuracy and reasonable cost, vision-based sensors are mostly used for seam tracking in most robotic weld applications, apart from through-the-arc  sensing。

The research-based project MARWIN presented a    semi-autonomous  robotic  weld  system  in  which  vision sensors

scan the work piece assembly in 3D using structured light, which is compared to the CAD drawing to calculate the robot trajectory and weld parameters from an inbuilt data- base。 This approach eliminates the necessity of tedious pro- gramming for robotic and welding parameters for each inpidual work part and the role of the user is limited to high-level specification of the welding task and confirm- ation and/or modification if required。 SMEs with small pro- duction volumes and varied workpieces stand to benefit greatly from such semi-autonomous robotic welding。

Until recently, most robot programs were only taught through the robot teach pendant, which required the robot system to be out of production。 Now, programmers are using offline program tools to teach the robot move- ments。 After transferring the program to the robot  con- 文献综述

troller, they use the robot teach pendant to refine the

Fig。 17 Structured light scanning method (Rodrigues et al。 2013a)program positions。 This greatly improves the productivity

of the robot system。 But still, calibration is needed be- tween the model and the real work cell。 The trend is the development of more intelligent programming, by use of sensors with the ability to scan the workpiece and working environment with high accuracy。摘要：机器人焊接上的技术创新和传感器控制功能上更大的可行性已经允许机器人焊接在充满热量和烟雾的艰难工作环境中代替手工焊接过程。工业机器人的使用或是大容量生产率的机械化设备已经变得越来越普遍，自动化金属极惰性气体保护电弧焊(GMAW)通常被使用。机器人焊接的普遍使用需要更大的能力去控制焊接参数，机器人运，并且改进故障检测和修正错误。半自动化机器人焊接(比如，高自动化的系统只需要较小的运营商的干预)面临许多问题，最普遍的就是需要补偿工件夹具上的不准缺性，工件尺寸的变化，缺陷边缘的准备，加工过程中因热量的变形。最大的挑战就是关节边缘上的检测，接头焊缝检测，熔透控制。这样的问题可以通过使用从焊缝反馈回来的传感器信号更有效地解决。因此，传感器在机器人弧焊带有自适应和智能控制系统特性可以跟踪监控进程内的焊缝质量，关节位置和几何变化中扮演了很重要的角色。本工作描述了机器人弧焊上的各个方面，可编程机器人焊接系统和相关的技术问题。进一步讨论了商业焊缝跟踪和seam-finding传感器，提出了一个实际案例应用半自治的传感器。这项研究增加了对机器人焊接的熟悉和传感器在机器人焊接的作用和他们相关的问题 弧焊机器人传感器英文文献和中文翻译(12):http://www.youerw.com/fanyi/lunwen_98156.html