Integrated Visual/Force Servoing Control of Robotic Manufacturing Systems

机器人制造系统的集成视觉/力伺服控制

• 批准号: RGPIN-2015-05434
• 负责人: Xie, WenFang
• 金额: $ 2.48万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=590425
• 关键词: Integrated; Visual; Force; Servoing; Control

The work proposed in this application for the next five years builds on my previous research on visual servoing control of robotic manufacturing systems which aims at increasing the accuracy and speed of robot positioning. Several advanced control strategies have been developed to achieve the goal in the robotic manufacturing systems. However, in addition to high precision positioning, the industry is also looking for safe and efficient control strategies that allow the robotic system performing tracking, positioning and grasping tasks etc. in an unstructured environment. Such control strategies can keep the robotic system up and running for the long haul and can avoid any damage when the robot crash occurred. Currently, the operator has to shut down production and try to fix the damages. For the high end manufacturing systems especially in aerospace industry, the crash will result in tooling damage and significant loss of the production time. For instance, in the advanced fiber placement (AFP) machine, the collision between the fiber place head and the mandrel will cost up to ten thousands dollar in repairing the roller or other parts in the fiber placement head. Although some passive solutions have been used in the industry such as installing a crash protection device between the end effector and the tool or designing a good motion planning controller, an active prevention solution that integrates the vision, force and position signals and provides the safe and efficient control strategies to the robotic manufacturing systems are sought by the industry. A lot of research has been devoted to vision and force control in the robotic control community. However, few approaches focus on dealing with the collision and crash in the real industrial manufacturing processes. Therefore, a new practical multi-sensor based control framework for collision avoidance is to be built to meet the demand from robotic manufacturing industry.

在此应用程序中提出的未来五年的工作建立在我之前对机器人制造系统的视觉伺服控制的研究基础上，旨在提高机器人定位的精度和速度。为了实现这一目标，在机器人制造系统中发展了几种先进的控制策略。然而，除了高精度定位之外，业界还在寻找安全高效的控制策略，使机器人系统能够在非结构化环境中执行跟踪，定位和抓取任务等。这样的控制策略可以使机器人系统长时间运行，并在机器人发生碰撞时避免任何损坏。目前，作业者不得不停止生产并试图修复损坏。对于高端制造系统，特别是航空航天工业，碰撞将导致模具损坏和生产时间的重大损失。例如，在先进的纤维放置机（AFP）中，在修复纤维放置头中的滚轮或其他部件时，纤维放置头与芯轴之间的碰撞将花费高达数万美元。虽然工业上已经采用了一些被动解决方案，如在末端执行器和工具之间安装碰撞保护装置或设计良好的运动规划控制器，但工业上正在寻求一种集成视觉、力和位置信号并为机器人制造系统提供安全高效控制策略的主动预防解决方案。机器人控制学界对视觉控制和力控制进行了大量的研究。然而，很少有方法专注于处理真实工业制造过程中的碰撞和碰撞。因此，为了满足机器人制造业的需求，需要构建一种新的实用的基于多传感器的避碰控制框架。