Exploring Human Hand Capabilities into Multifingered Robot Manipulation

探索多指机器人操作的人手能力

• 批准号: EP/G041377/1
• 负责人: Honghai Liu
• 金额: $ 36.22万
• 依托单位: University of Portsmouth
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FG041377%2F1
• 关键词: Exploring; Human; Hand; Capabilities; into

It is evident that service robotics has the potential to improve people's quality of life and it holds the key to a number of unmet applications related to health care and rehabilitation. According to the prediction of International Federation of Robotics, the global market for intelligent service robots is forecast to reach 24.3 billion USD worldwide by 2010. A multi-fingered robotic hand is the most complex and dexterous robotic system, whose development represents frontiers in service robotics research. Recent innovations in motor technology and robotics have achieved impressive results in the hardware of robotic hands such as Robonaut hand. However, the manipulation systems of robotic hands are hardcoded to handle specific objects in specific ways, which significantly limits their transfer to a range of different situations and applications. The control and optimisation problems involved in robot hand manipulation are very difficult to solve in mathematical terms, however humans solve their hand manipulation related tasks easily using skill and experience. Object manipulation algorithms are required to meet the market requirement that robot hand systems should have human-like manipulation capabilities and be independent of robot hand hardware. Hence, the main challenge that researchers now face is how to enable robot hands to use what can be learned from human hands, to manipulate objects, with the same degree of skill and delicacy as human hands. The proposed work aims to investigate artificial intelligence (AI) methodologies and practical solutions which will allow robotic hands to automatically adapt to human environments and thus to enable them to autonomously perform useful manipulation tasks involved in daily living, pontentially for health care and rehabilitation applications. The investigation will focus on the following areas. 1) To generate a series of responsive human-like finger gaits for a robotic hand given an object to manipulate. This will have the capability to iteratively build a knowledge base representing the features of human hand manipulation behaviour and to efficiently provide corresponding robot hand gaits and manipulation strategies for a given manipulation task in a human environment.2) To develop feasible friction models for the interaction of objects and a robot/human hand. This will enable the application of existing mathematical research findings in multifingered robot manipulation to realworld applications in human environments and will integrate related methods in engineering and AI domains. 3) To develop an AI-based control architecture to ensure robust object manipulation of multifingered robots in terms of manipulation feasibility and efficiency. This will allow robot hands to perform stable human-like object grasping and manipulation and will also provide an open architecture which has the potential to introduce human brain (EEG/MRI signals) and human muscles (EMG signals) information into robotic hand systems.4) To validate the proposed algorithms by implementing these into a set of defined scenarios with a set of simulated multifingered robot hands and three different types of physical robot hands.

显然，服务机器人技术有可能改善人们的生活质量，并且它是许多与医疗保健和康复有关的未满足应用程序的关键。根据国际机器人技术联合会的预测，预计到2010年，全球智能服务机器人市场将在全球范围内达到243亿美元。多指的机器人手是最复杂，最灵敏的机器人系统，其发展代表了服务机器人研究的前沿。机器人技术和机器人技术的最新创新在机器人手（例如Robonaut Hand）的硬件中取得了令人印象深刻的结果。但是，机器人手的操纵系统是用特定方式处理特定对象的，这大大限制了它们转移到一系列不同的情况和应用中。机器人手工操纵所涉及的控制和优化问题很难用数学术语来解决，但是人类使用技能和经验轻松地解决了与手动操纵相关的任务。需要对象操作算法满足市场要求，即机器人手系统应具有类似人类的操纵功能，并且独立于机器人手工硬件。因此，研究人员现在面临的主要挑战是如何使机器人手从人手中学到的东西，以与人类手相同的技巧和美味来操纵物体。拟议的工作旨在调查人工智能（AI）方法和实用解决方案，这些方法和实用解决方案将使机器人手能够自动适应人类环境，从而使它们能够自主执行参与日常生活的有用操纵任务，以实现医疗保健和康复应用。调查将集中在以下领域。 1）为机器人手生成一系列反应灵敏的人类手指步态，以操纵对象。这将具有迭代建立一个知识基础的能力，该知识基础代表人类手动操纵行为的特征，并有效地为人类环境中给定的操纵任务提供相应的机器人手步态和操纵策略。这将使现有的数学研究发现在多方面的机器人操纵中应用于人类环境中的现实世界应用，并将相关方法整合在工程和AI领域中。 3）开发基于AI的控制体系结构，以确保在操纵性和效率方面对多方面的机器人进行牢固的对象操纵。这将使机器人手能够执行稳定的人类对象抓握和操纵，还将提供一个开放的体系结构，有可能将人脑（EEG/MRI信号）和人类肌肉（EMG信号）（EMG信号）信息引入机器人手部系统中。

项目成果

FUZZY QUALITATIVE REASONING ABOUT DYNAMIC SYSTEMS CONTAINING TRIGONOMETRIC RELATIONSHIPS

含三角关系的动态系统的模糊定性推理

• DOI: 10.1142/s021848851100709x
• 发表时间: 2011
• 期刊: International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
• 作者: COGHILL G

Intelligent computation in grasping control of dexterous robot hand

灵巧机械手抓取控制的智能计算

• DOI: 10.1166/jctn.2015.4642
• 发表时间: 2015-12
• 期刊: Journal of Computational and Theoretical Nanoscience
• 作者: Weiliang Ding;Gongfa Li;Guozhang Jiang;Yinfeng Fang;Zhaojie Ju;Honghai Liu
• 通讯作者: Honghai Liu

A New Wearable Ultrasound Muscle Activity Sensing System for Dexterous Prosthetic Control

• DOI: 10.1109/smc.2015.251
• 发表时间: 2015-01-01
• 期刊: 2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS
• 作者: Hettiarachchi, Nalinda;Ju, Zhaojie;Liu, Honghai
• 通讯作者: Liu, Honghai

Intelligent computational control of multi-fingered dexterous robotic hand

• DOI: 10.1166/jctn.2015.4647
• 发表时间: 2015-12
• 期刊: Journal of Computational and Theoretical Nanoscience
• 作者: Chen Disi;Gongfa Li;Guozhang Jiang;Yinfeng Fang;Zhaojie Ju;Honghai Liu

A Multichannel Surface EMG System for Hand Motion Recognition

• DOI: 10.1142/s0219843615500115
• 发表时间: 2015-06-01
• 期刊: INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS
• 影响因子: 1.5
• 作者: Fang, Yinfeng;Liu, Honghai;Zhu, Xiangyang
• 通讯作者: Zhu, Xiangyang