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: 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=587589
• 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、制造业、信息和信号处理、控制和人工智能等多个学科的技术和科学进步推动了机器人传统和新型应用的快速增长。机器人系统越来越多地用于工业自动化、灾难恢复、搜索和救援、危险材料和废物处理、采矿、太空和水下作业以及医学。尽管有这些进步，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。 这项研究涉及人在环机器人系统。这些是人类和机器人合作完成任务的系统。 这项研究的总体目标是弥合自主机器人和远程机器人两个领域之间的现有差距。传统上，这些领域的研究主要遵循两条独立的道路。自主机器人系统大多由完全自主的机器人组成。另一方面，遥控机器人系统主要涉及一个操作员完全控制一个机器人操纵器的系统配置。 这项研究追求一种新的模式，在系统设计和控制中，远程机器人和自主机器人的元素相结合。在这种新的范式中，操作员控制任务的各个方面，这些方面将受益于人类独特的认知和决策能力。同时，机器人通过自主控制任务的更多结构化方面来辅助操作员，以提高精度并减少认知负荷。 拟议的研究将寻求一个通用的框架，控制和协调混合自主机器人/远程机器人，适用于广泛的系统配置。控制策略将基于一种新型的多级控制架构。在一个级别上，处理任务的自主方面的基于优化的模型预测控制器将产生命令，然后将其传递到另一个控制级别。该下一级控制器将结合联合收割机自主和常规遥控操作控制，并将根据用户定义的任务优先级解决两者之间的任何潜在冲突。在作出最佳控制决策时，将考虑操作员未来行动和任务环境的其他要素的不确定性。这将使用基于粒子的估计和优化技术来实现。预计所得到的估计和控制算法的实时计算要求将超过最先进的台式计算机的能力。并行实现的图形处理器单元的算法将被追求，以帮助实现使用廉价的，现成的，图形卡的时序要求。 这项研究的成果将有助于机器人工程师和科学家设计出联合收割机，结合远程操作和自主控制的好处。这样的系统将能够在复杂的非结构化环境中更有效地运行，这在许多机器人应用中很常见。从这项研究中产生的新知识将在对加拿大非常重要的领域，如医疗保健，制造业和汽车工业，采矿业和太空等领域中找到新的技术和产品。加拿大是机器人领域的主要参与者，该领域的加拿大公司将受益于新知识以及参与研究的高素质人员。