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.

在过去的几十年中，越来越多的关注对机器人灵巧的操纵进行了广泛的应用，例如制造业，军事应用，仓库，手术和假肢和医疗保健服务等广泛的应用程序。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仍然远非人类。拟议的工作涉及机器人技术和自动化方面公认的主要挑战之一。拟议研究的结果将使机器人系统与人类相互作用的机器人系统的更可靠，适应性和有效的操作。合作操作的研究将有助于该行业提供便利，并提高经济效率，并为需要医疗干预和身体援助的人们提供好处。 短期目标是实现：1）对单臂的触觉和合规操作，以改善配备握把的手和触觉感测的操纵器的灵巧性； 2）在复杂的环境中为任务的灵巧合作机器人操作，需要双人和多个机器人操纵，例如拧紧，去除危险材料，手术和装配任务； 3）智能的人类机器人灵巧的共同操作，以提高人类安全，生产力并促进人类指导的行为学习。 将通过广泛的实验研究研究以下问题。机器人手将与7-DOF操纵器集成，以改善操纵性和灵巧性。通过力量控制，合规性的操纵，自由运动的新协调控制，与环境互动时的新型适应性入学控制以及与基于学习的方法的比较。在复杂的环境中避免碰撞的多个操纵器之间的合作操纵，将通过适当的性能指数开发，优化和评估合作任务上的力量/运动协调方案。将开发人机界面，以预测人类合作伙伴对人类机器人协作任务的目标。将提出有效的人类机器人共同操作方法，多模式，稳定且可靠的人类手机灵巧操纵以及在复杂环境中为人类机器人共采用的新的强化学习方法。这项研究将为高素质的人员提供跨学科的培训环境，具有更大的动手能力，可以为机器人技术，控制，机电货量，信号处理和AI的关键技术领域做出贡献。发展高科技自动化和机器人行业有可能为加拿大经济做出重大贡献，这项研究的结果将增强加拿大在行业自动化和智能系统方面的概况。