Integrated Visual/Force Servoing Control of Robotic Manufacturing Systems

这项申请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，该研究旨在提高机器人定位的精度和速度。为了在机器人制造系统中实现这一目标，已经开发了几种先进的控制策略。然而，除了高精度定位外，该行业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、定位和抓取等任务。这样的控制策略可以保持机器人系统的长期启动和运行，并可以避免机器人发生碰撞时的任何损害。目前，运营商不得不关闭生产，并试图修复损坏。对于高端制造系统来说，尤其是在航空航天领域，崩溃会导致工装损坏和生产时间的重大损失。例如，在先进的纤维铺放(AFP)机器中，纤维铺放头与芯棒之间的碰撞将花费高达1万美元来修理纤维铺放头中的滚筒或其他部件。虽然工业上已经使用了一些被动的解决方案，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，但业界一直在寻求一种集视觉、力和位置信号于一体的主动预防方案，为机器人制造系统提供安全高效的控制策略。在机器人控制界，很多研究都致力于视觉和力控制。然而，很少有方法关注于处理实际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，需要建立一种新的实用的基于多传感器的避碰控制框架。

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.