Model Predictive Control Strategies for Mixed Telerobotic/Autonomous Robotic Systems

混合远程机器人/自主机器人系统的模型预测控制策略

• 批准号: RGPIN-2014-04078
• 负责人: Sirouspour, Shahin
• 金额: $ 3.06万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=610819
• 关键词: Model; Predictive; Control; Strategies; Mixed

In recent years, technological and scientific advances in a number of disciplines such as mechatronics, manufacturing, information and signal processing, controls, and artificial intelligence have fuelled a rapid growth in traditional and new applications of robotics. Robotic systems are increasingly used in industrial automation, disaster recovery, search and rescue, hazardous material and waste handling, mining, space and underwater operations, and medicine. Notwithstanding all these advances, robots may not still be able to operate fully autonomously in many complex uncertain task environments, and do often require some form of human supervision/control. This research is concerned with human-in-the-loop robotic systems. These are systems in which human(s) and robot(s) work cooperatively to accomplish a task. The overall goal of the research is to bridge an existing gap between the two fields of autonomous robotics and telerobotics. Traditionally, research in these areas has followed two mostly separate paths. Autonomous robotics systems have mostly comprised of fully autonomous robots. Telerobotics systems, on other hand, have mainly involved system configurations in which one operator fully controls one robotic manipulator. This research pursues a new paradigm in system design and control in which elements of telerobotics and autonomous robotics are combined. In this new paradigm, the operator(s) control aspects of the task that would benefit from human’s unique cognition and decision-making capabilities. Meanwhile, robot(s) assist the operator(s) by autonomously controlling more structured aspects of the task to improve precision and reduce cognitive load. The proposed research will seek a general framework for control and coordination in mixed autonomous robotics/teleorobotics, applicable to a broad class of system configurations. The control strategies will be based on a novel multiple-level control architecture. At one level, optimization-based model-predictive controllers that deal with autonomous aspects of the task will produce commends, which will then be passed to another level of control. This next level controller will combine autonomous and conventional teleoperation control and will resolve any potential conflicts between the two based on user-defined task priorities. Uncertainty in operator(s) future actions and other elements of the task environment will be considered in making optimal control decisions. This will be achieved using particle-based estimation and optimization techniques. It is expected that the real-time computational requirements of the resulting estimation and control algorithms will exceed capabilities of state-of-art desktop computers. Parallel implementation of the algorithms on Graphic Processor Units will be pursued to help achieve the timing requirements using inexpensive, off-the-shelf, graphics cards. The outcomes of this research will help robotics engineers and scientists to design systems that combine benefits of teleoperation and autonomous control. Such systems will be able to operate more effectively in complex unstructured environments, which are common in many robotics applications. The new knowledge arising from this research will find its way into new technologies and products in areas of great importance to Canada such as healthcare, manufacturing and auto industry, mining, and space. Canada is a major player in the field of robotics and Canadian companies in this area will benefit from the new knowledge and also the highly qualified personnel that will participate in the research.

近年来，机电一体化、制造、信息与信号处理、控制、人工智能等学科的技术和科学进步，推动了机器人技术在传统和新兴应用领域的快速发展。机器人系统越来越多地应用于工业自动化、灾难恢复、搜索和救援、危险物质和废物处理、采矿、太空和水下作业以及医学。尽管有了这些进步，机器人可能仍然无法在许多复杂的不确定任务环境中完全自主地操作，并且通常需要某种形式的人类监督/控制。