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Development of autonomous mobile robot with manipulator for manufacturing environment

机译:具有制造环境的机械手自主移动机器人的开发

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This paper describes the developmental effort involved in prototyping the first indigenous autonomous mobile robot, AMR, with a manipulator for carrying out tasks related to manufacturing. The objective is to design and develop a vehicle that can navigate autonomously and transport jobs and tools in a manufacturing environment. Proprioceptive and exteroceptive sensors are mounted on AMR for navigation. Among the exteroceptive sensors, a stereovision camera is mounted in front of AMR for mobile robot perception of the environment. Using the widely supported JPEG image file format, full high-resolution color images are transmitted frame by frame from the mobile robot to multiple viewers located within the robot work area, where fast reconstruction of these images enables remote viewing. A CMOS camera mounted on the manipulator identifies jobs for pick-and-place operation. A variation of correlation based adaptive predictive search (CAPS) method, a fast search algorithm in template matching, is used for job identification. The CAPS method justifiably selects a set of search steps rather than consecutive point-to-point search for faster job identification. Search steps, i.e., either coarse search or fine search, are selected by calculating the correlation coefficient between template and the image. Adaptive thresholding is used for image segmentation for parametric calculations required for proper gripping of the object. Communication with the external world allowing remote operation is maintained through wireless connectivity. It is shown that autonomous navigation requires synchronization of different processes in a distributed architecture, while concurrently maintaining the integrity of the network.
机译:本文描述了开发第一个本地自主移动机器人AMR的原型机的开发工作,该机器人配有一个机械手来执行与制造相关的任务。目的是设计和开发一种能够在制造环境中自动导航并运输工作和工具的车辆。本体感受和外部感受传感器安装在AMR上以进行导航。在外感传感器中,立体视觉摄像机安装在AMR的前面,用于移动机器人感知环境。使用广泛支持的JPEG图像文件格式,将完整的高分辨率彩色图像从移动机器人逐帧传输到位于机器人工作区域内的多个查看器,在这些查看器中,这些图像的快速重建可实现远程查看。安装在机械手上的CMOS摄像头可识别用于拾取和放置操作的作业。一种基于相关性的变体自适应预测搜索(CAPS)方法,一种模板匹配中的快速搜索算法,用于工作识别。 CAPS方法合理地选择了一组搜索步骤,而不是连续的点对点搜索,以便更快地识别作业。通过计算模板和图像之间的相关系数来选择搜索步骤,即粗略搜索或精细搜索。自适应阈值用于图像分割,以进行正确抓取对象所需的参数计算。通过无线连接可以保持与外界的通讯,从而实现远程操作。结果表明,自主导航需要在分布式体系结构中同步不同的过程,同时保持网络的完整性。

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