Versatile and Dexterous Adaptive Control of Dual-Arm Robots for Personalized Assistance

双臂机器人的多功能灵巧自适应控制，提供个性化协助

• 批准号: EP/L026856/1
• 负责人: Chenguang Yang
• 金额: $ 11.84万
• 依托单位: Plymouth University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FL026856%2F1
• 关键词: Versatile; Dexterous; Adaptive; Control; Dual

In the near future, robots are expected to co-habit with human beings and work closely with us in the fields of manufacturing and medicine as well as in other aspects of our daily lives. Unfortunately, most of the current robot control technologies are designed for industrial robots which operate behind safeguarding and for predefined tasks, and thus are not able to cope with the varying tasks in unknown dynamic environments. This project will develop human-like adaptive control techniques to provide versatile and dexterous assistance to humans . In particular, the proposed new control techniques will enable robots to offer personalized assistance to different human users according to their motion patterns. For example, when the robot is interacting with an elderly person, it will automatically slow down; while it would speed up when working with a young man. To achieve these goals, this project will perform physiological studies on human control in the following two aspects: i) apply advanced control theory to analyze bimanually coordinated control strategies of human-beings in various scenarios; ii) investigate how human users develop efficient cooperation with a robot from a machine learning point of view . The establishment of human control models can help us in at least two aspects: developing human-like adaptive controllers with high versatility and dexterity; and personalizing robot control to suit a specific human user. Based on this understanding on human bimanual control and human-human cooperation, this project will: (1) enable the robot to cope with different objects by bimanual haptic exploration; (2) enhance the robustness of robot control in interacting with different human users and in handling various objects; (3) improve robot manipulative dexterity at human-robot interaction scenarios and under geometric constraints/limitations caused by hardware and safety issues; (4) detect motion intention of human user and enhance dexterity with robot joint compliance; (5) introduce a measurement of user experience to quantify how human users are satisfied with the assistance offered by robots; and (6) enable the robot to identify and adapt to motor behavior patterns of a specific human user.In the short term, this project will provide new control technologies for robotics researchers and builders to achieve better manipulation performance in terms of dexterity, versatility, and personalization. In the long run, it will benefit socially and economically to the UK by helping to boost the personal robot industry. It will further make contributions to UK by advancing its technology in the field of Robotics and Autonomous Systems, which is identified by the UK government as one of eight great British Technologies propelling the UK towards future growth.

在不久的将来，机器人有望与人类共存，并在制造和医药领域以及我们日常生活的其他方面与我们密切合作。不幸的是，目前大多数机器人控制技术都是为工业机器人设计的，这些机器人在保护和预定义的任务之后运行，因此无法应对未知动态环境中的变化任务。该项目将开发类似人类的自适应控制技术，为人类提供多功能和灵巧的帮助。特别是，所提出的新控制技术将使机器人能够根据不同的人类用户的运动模式提供个性化的帮助。例如，当机器人与老人互动时，它会自动减速；而和年轻人一起工作时，速度会加快。为实现这一目标，本项目将从以下两个方面开展人体控制的生理学研究：i)应用先进的控制理论，分析不同场景下人类的手动协调控制策略；Ii)从机器学习的角度研究人类用户如何与机器人进行有效的合作。人类控制模型的建立至少可以在两个方面帮助我们：开发具有高通用性和灵巧性的类人自适应控制器；以及个性化机器人控制以适应特定的人类用户。基于对人的双手控制和人与人之间的合作的理解，本项目将：(1)通过双手的触觉探索使机器人能够应对不同的物体；(2)增强机器人控制在与不同人类用户交互和处理各种物体时的鲁棒性；(3)在人机交互场景下，在硬件和安全问题的几何约束/限制下，提高机器人的操作灵巧度；(4)检测人类使用者的运动意图，利用机器人关节的顺应性增强灵巧性；(5)引入用户体验测量，量化人类用户对机器人提供的帮助的满意程度；以及(6)使机器人能够识别并适应特定人类用户的运动行为模式。在短期内，该项目将为机器人研究人员和建造者提供新的控制技术，以实现更好的灵巧性，多功能性和个性化的操作性能。从长远来看，通过帮助推动个人机器人产业，它将有利于英国的社会和经济。它将通过推进英国在机器人和自主系统领域的技术，进一步为英国做出贡献，这被英国政府确定为推动英国未来发展的八大英国技术之一。

项目成果

Neural network based reinforcement learning control of autonomous underwater vehicles with control input saturation

• DOI: 10.1109/control.2014.6915114
• 发表时间: 2014-07
• 期刊: 2014 UKACC International Conference on Control (CONTROL)
• 作者: Rongxin Cui;Chenguang Yang;Y. Li;Sanjay K. Sharma

DMP and GMR based teaching by demonstration for a KUKA LBR robot

• DOI: 10.23919/iconac.2017.8081982
• 发表时间: 2017
• 期刊: 2017 23rd International Conference on Automation and Computing (ICAC)
• 作者: Alexander Hewitt;Chenguang Yang;Yong Li;Rongxin Cui

Innovative adaptive autopilot design for uninhabited surface vehicles

• DOI: 10.1049/cp.2014.0677
• 发表时间: 2014-09
• 期刊: Electronics
• 影响因子: 2.9
• 作者: A. Annamalai;R. Sutton;Chenguang Yang;P. Culverhouse;Sanjay K. Sharma

Model Identification and Control Design for a Humanoid Robot

仿人机器人模型识别与控制设计

• DOI: 10.1109/tsmc.2016.2557227
• 发表时间: 2017
• 期刊: Ieee Transactions ON Systems Man Cybernetics-Systems
• 影响因子: 8.7
• 作者: He Wei;Ge Weiliang;Li Yunchuan;Liu Yan-Jun;Yang Chenguang;Sun Changyin
• 通讯作者: Sun Changyin

Towards Autonomous Robotic Systems

迈向自主机器人系统

• DOI: 10.1007/978-3-319-40379-3_8
• 发表时间: 2016
• 作者: Dennis L