Robust, Cooperative and Intelligent Dexterous Manipulation in Complex Environments

复杂环境下的鲁棒、协作、智能灵巧操控

• 批准号: RGPIN-2021-02406
• 负责人: Pan, YaJun
• 金额: $ 2.33万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741957
• 关键词: Robust; Cooperative; Intelligent; Dexterous; Manipulation

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.

在过去的几十年里，机器人灵巧操作在广泛的应用中受到了越来越多的关注，如制造、军事应用、仓库、手术和假肢以及医疗保健服务中的智能工厂。尽管机器人学、人工智能和计算技术取得了重大进步，但机器人执行一般任务的能力仍远不及人类。拟议的工作解决了机器人和自动化领域公认的主要挑战之一。拟议的研究结果将使与人类互动的机器人系统能够更可靠、更具适应性和更高效地运行。协同手法的研究将有助于该行业为需要医疗干预和身体帮助的人提供操作便利和提高经济效益，并使其受益。短期目标是实现：1)单臂的触觉和顺应操作，以提高配备抓手和触觉传感的机械手的灵活性，用于制造和医疗保健应用；2)灵活协作的机器人操作，在需要双手和多机器人操作的复杂环境中执行任务，如拧紧、移除危险材料、手术和装配任务；3)智能人-机器人灵活协作操作，以提高人类的安全性、生产率和促进人类引导的行为学习。将通过广泛的实验研究来研究以下问题。机械手将与七自由度机械手集成在一起，以提高机械手的可操作性和灵活性。将开发与力控制相适应的仿人操作、自由运动的新协调控制、与环境交互时的新自适应导纳控制，并与基于学习的方法进行比较。在复杂环境下多机械手之间的协作操作中，针对协作任务中的力/运动协调问题，设计、优化和评价了合适的性能指标。将开发一个人机界面，以预测人类伙伴在人-机器人协作任务中的目标。针对复杂环境下的人-机器人协同操作，提出了有效的人-机器人协同操作方法、多通道、稳定、健壮的人-机器人灵巧操作方法和新的强化学习方法。该研究将为具有更多动手能力的高素质人员提供跨学科培训环境，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产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形象。