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Integrated image processing and path planning for robotic sketching

机译:机器人素描的集成图像处理和路径规划

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Since the beginning of the development of machine vision, researchers have realized its importance in the robotics field, as it provides a useful tool for both the environment detection and decision making during the automation process. At the same time, path planning for robots influences many in the robotics and automation field and it has remained active for both methodology research and system implementation. This research combines machine vision with robot path planning with an aim of programming-free robotic applications. Particularly as a proof of concept, a programming-free robotic sketching prototype is developed as a case study. Within the context, this paper consists of three parts. The first part covers the processing of a facial image taken by a webcam to identify the contours that represent the image; the second part converts these contours to paths for an industrial robot to follow, and the third part controls the robot adaptively for sketching including auto-generation of control codes and self-calibration. The developed prototype is a closed-loop system with networked camera and robot. Intelligent computation is applied to identify the contours of the image with minimum representation of points and with the correct sequence of points for each curve (path); the sequence of the output robot paths represents the near-optimal sequence to preserve the minimum travelling time for the robot. The robot control module can also retrieve the TCP of the robot for off-site monitoring. The ultimate goal of this research is future applications of robot path following, e.g. ad-hoc robotic cutting or welding where the paths can be specified by hand-drawings of an operator on the target workpiece with zero programming for the operator.
机译:自机器愿景开发以来,研究人员在机器人领域实现了重要性,因为它为在自动化过程中提供了一个有用的工具,为环境检测和决策提供了一种有用的工具。与此同时,机器人的路径规划会影响机器人和自动化领域的许多,并且它对方法研究和系统实现仍然活跃。本研究将机器愿景与机器人路径规划相结合,目的是无编程的机器人应用。特别是作为概念证据,开发了一种无编程的机器人素描原型作为案例研究。在下文中,本文包含三个部分。第一部分覆盖了由网络摄像头拍摄的面部图像的处理,以识别代表图像的轮廓;第二部分将这些轮廓转换为要遵循的工业机器人的路径,第三部分适用于自适应地控制机器人,包括自动生成控制代码和自校准。开发的原型是具有网络相机和机器人的闭环系统。应用智能计算以识别图像的最小表示和每个曲线的正确点序列(路径);输出机器人路径的序列表示近最佳序列,以保留机器人的最小行进时间。机器人控制模块还可以检索机器人的TCP以进行非现场监控。这项研究的最终目标是机器人路径的未来应用,例如: ad-hoc机器人切割或焊接,其中路径可以通过操作员上的操作员的手绘,为操作员为零编程。

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