Robust, Cooperative and Intelligent Dexterous Manipulation in Complex Environments

在过去的几十年里，人们越来越关注机器人灵巧操作的广泛应用，如制造业中的智能工厂、军事应用、仓库、外科和假肢以及医疗保健服务。尽管机器人技术、人工智能和计算技术取得了重大进展，但机器人执行一般任务的能力与人类相比仍有很大差距。提出的工作解决了机器人和自动化领域公认的主要挑战之一。所提出的研究结果将使机器人系统与人类互动的操作更加可靠，适应性更强，效率更高。合作操作的研究将有助于行业提供操作便利，提高经济效率，并使需要医疗干预和身体援助的人受益。短期目标是实现：1)单臂的触觉和柔顺操纵，以提高机械臂的灵活性，为制造业和医疗保健应用配备抓握手和触觉传感；2)灵巧协作机器人操作，适用于需要双手和多个机器人操作的复杂环境任务，如旋紧、移除有害物质、手术和装配任务；3)智能人机灵巧协同操作，提高人类的安全性、生产力，促进人类引导的行为学习。以下问题将通过广泛的实验研究进行研究。机器人手将与七自由度机械臂集成，以提高机械臂的可操控性和灵巧性。将开发具有力控制的柔性类人操作，自由运动的新型协调控制，与环境交互时的新型自适应导纳控制，以及与基于学习的方法的比较。复杂环境下多机械臂间避碰协同操作，协同任务的力/运动协调方案将被开发、优化并以适当的性能指标进行评估。将开发人机界面，以预测人机协作任务中人类伙伴的目标。提出了有效的人机协同操作方法、多模态、稳定和鲁棒的人机灵巧操作方法以及复杂环境下人机协同操作的强化学习新方法。该研究将为具有更多动手能力的高素质人才提供跨学科的培训环境，为机器人，控制，机电一体化，信号处理和人工智能等关键技术领域的创新做出贡献。发展高科技自动化和机器人工业有可能对加拿大经济做出重大贡献，这项研究的结果将提高加拿大在工业自动化和智能系统方面的地位。

Over the past decades, increasing attention has been paid to robotic dexterous manipulation for a broad range of applications, such as smart factories in manufacturing, military applications, warehouses, surgery and prosthetics, and health care services. Despite the significant progress of robotics, artificial intelligence and computing technologies, the ability of robots to perform general tasks is still far from that of human beings. The proposed work addresses one of the recognized major challenges in robotics and automation. The results of the proposed research will enable more reliable, adaptable, and efficient operation of robotic systems interacting with humans. The research in cooperative manipulation will help the industry to offer operating convenience and increase economic efficiency as well as benefit to people requiring medical interventions and physical assistance. The short term objectives are to achieve: 1) tactile and compliant manipulation of a single arm to improve the dexterity for manipulators equipped with grasping hands and tactile sensing for manufacturing and health care applications; 2) dexterous cooperative robotic manipulation for tasks in a complex environment where bimanual and multiple robotic manipulations are required, such as screwing, removal of hazardous materials, surgery, and assembly tasks; 3) intelligent human-robot dexterous co-manipulation to improve human safety, productivity, and facilitate human-guided behavior learning. The following issues will be studied with extensive experimental studies. Robotic hands will be integrated with 7-DOF manipulators to improve the manipulability and the dexterity. Compliant human-like manipulations with force control, new coordination control for free motion, novel adaptive admittance control when interacting with the environment, and comparisons with learning-based approach will be developed. Cooperative manipulation between multiple manipulators with collision avoidance in a complex environment, force/motion coordination schemes on cooperative tasks will be developed, optimized and evaluated with proper performance indices. A human-machine interface to anticipate the objective of the human partner for human-robot collaboration tasks will be developed. Effective human-robot co-manipulation approaches, multi-modal, stable and robust human-robot dexterous manipulation and new reinforcement learning approach for human-robot co-manipulation in complex environments will be proposed. The research will provide an interdisciplinary training environment to highly qualified personnel with more hands-on ability to contribute to innovation in the key technological areas in robotics, control, mechatronics, signal processing and AI. Developing the high-technology automation and robotic industry has the potential to contribute significantly to the Canadian economy and the outcomes of this research will enhance Canada's profile in industry automation and intelligent systems.