共融机器人重大研究计划研究进展

doi:10.3901/JME.2021.23.001

摘要/Abstract

摘要： “共融机器人基础理论与关键技术研究”重大研究计划，聚焦“刚-柔-软机构的行为顺应与可控性”、“人-机-环境多模态感知与自然交互”以及“机器人群体智能与操作系统架构”三个科学问题，主要面向智能制造、医疗康复、国防安全和学科前沿四个重点方向，实施四年来取得了长足进展，构建机器人与人、机器人与环境、机器人与机器人交互的共融机器人研究体系，推动了工业机器人、服务机器人和特种机器人领域的理论和技术发展，相关科研成果为我国高端制造、康复医疗、国防安全等国家重大需求提供了坚实的理论和技术支撑。对在该重大研究计划支持下所取得的有关共融机器人的主要研究进展进行概述，内容涵盖“与环境共融的加工制造机器人”、“与人共融的康复辅助机器人”、“机器人间共融的群体智能与操作系统”以及“共融机器人前沿：新方法、新原理和新概念”，以此给出后续的重点研究方向和建议。

关键词: 机器人, 共融, 协作, 交互, 智能

Abstract: The major research plan on "Research on Basic Theory and Key Technology of Tri-Co Robots" focuses on three scientific issues:"behavior compliance and controllability of rigid-flexible-soft mechanism", "human-machine-environment multimodal perception and natural interaction" and "robotic swarm intelligence and operating system architecture", and mainly pays attention to four key directions:intelligent manufacturing, medical rehabilitation, national defense security and discipline frontiers. Over the past four years, great progress has been made in the implementation of the project. The Tri-Co Robots research system has been established for the interaction between robot and human, robot and environment, and that among robots, which has promoted the theoretical and technological development in the fields of industrial robots, service robots and special robots. The relevant scientific research achievements provide solid theoretical and technological support for high-end manufacturing, rehabilitation and medical treatment, national defense and security, and other major demands. This paper mainly summarizes the main research progress on Tri-Co Robots made under the support of this major research plan, including "processing and manufacturing robots integrated with environment", "rehabilitation auxiliary robots integrated with people", "group intelligence and operating system integrated between robots" and "frontiers of Tri-Co Robots:new methods, new principles and new concepts". This study gives the follow-up key research directions and suggestions.

Key words: robot, coexisting-cooperative-cognitive, collaboration, interaction, intelligence

中图分类号:

TP24

赖一楠, 叶鑫, 丁汉. 共融机器人重大研究计划研究进展[J]. 机械工程学报, 2021, 57(23): 1-11,20.

LAI Yinan, YE Xin, DING Han. Research Progress of Major Research Plan on Tri-Co Robots[J]. Journal of Mechanical Engineering, 2021, 57(23): 1-11,20.

参考文献

[1] DING Han,YANG Xuejun,ZHENG Nanning,et al. Tri-co robot:A Chinese robotic research initiative for enhanced robot interaction capabilities[J]. National Science Review,2018,5(06):799-801.
[2] TAO Bo,ZHAO Xingwei,DING Han. Mobile-robotic machining for large complex components:A review study[J]. Science China Technological Sciences,2019,62(8):1388-1400.
[3] 谢福贵,梅斌,刘辛军,等. 一种大型复杂构件加工新模式及新装备探讨[J]. 机械工程学报,2020,56(19):70-78. XIE Fugui,MEI Bin,LIU Xinjun,et al. Novel mode and equipment for machining large complex components[J]. Journal of Mechanical Engineering,2020,56(19):70-78.
[4] LUO Xuan,XIE Fugui,LIU Xinjun,et al. Kinematic calibration of a 5-axis parallel machining robot based on dimensionless error mapping matrix[J]. Robotics and Computer-Integrated Manufacturing,2021,70:102-115.
[5] XIE Zenghui,XIE Fugui,LIU Xinjun,et al. Tracking error prediction informed motion control of a parallel machine tool for high-performance machining[J]. International Journal of Machine Tools & Manufacture,2021,164:103714.
[6] BI Qingzhen,WANG Xinzhi,WU Qi,et al. Fv-SVM-based wall-thickness error decomposition for adaptive machining of large skin parts[J]. IEEE Transactions on Industrial Informatics,2018,15(4):2426-2434.
[7] ZHU Dahu,FENG Xiaozhi,XU Xiaohu,et al. Robotic grinding of complex components:a step towards efficient and intelligent machining-challenges,solutions,and applications[J]. Robotics and Computer-Integrated Manufacturing,2020,65:101908.
[8] ZHAO Xingwei,TAO Bo,HAN Shibo,et al. Accuracy analysis in mobile robot machining of large-scale workpiece[J]. Robotics and Computer-Integrated Manufacturing,2021,71:102153.
[9] CHEN Chen,CHAI Guohong,GUO Weichao,et al. Prediction of finger kinematics from discharge timings of motor units:Implications for intuitive control of myoelectric prostheses[J]. Journal of Neural Engineering,2019,16(2):026005.
[10] CHEN Chen,MA Shihan,SHENG Xinjun,et al. Adaptive real-time identification of motor unit discharges from non-stationary high-density surface electromyographic signals[J]. IEEE Transactions on Biomedical Engineering,2020,67(12):3501-3509.
[11] CHEN Chen,YU Yang,SHENG Xinjun,et al. Simultaneous and proportional control of wrist and hand movements by decoding motor unit discharges in real time[J]. Journal of Neural Engineering,2021,18(5):056010.
[12] HU Xuhui,ZENG Hong,SONG Aiguo,et al. Robust continuous hand motion recognition using wearable array myoelectric sensor[J]. IEEE Sensors Journal,2021,21(18):20596-20605.
[13] XU Baoguo,WANG Yong,DENG Leying,et al. Decoding hand movement types and kinematic information from electroencephalogram[J]. IEEE Trans Neural Syst Rehabil Eng,2021,29:1744-1755.
[14] ZHENG Enhao,WANG Qining. Noncontact capacitive sensing-based locomotion transition recognition for amputees with robotic transtibial prostheses[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2017,25(2):161-170.
[15] CREA S,MANCA S,PARRI A,et al. Controlling a robotic hip exoskeleton with noncontact capacitive sensors[J]. IEEE/ASME Transactions on Mechatronics,2019,24(5):2227-2235.
[16] ZHENG Enhao,WANG Qi,QIAO Hong. Locomotion mode recognition with robotic transtibial prosthesis in inter-session and inter-day applications[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2019,27(9):1836-1845.
[17] CHAI Guohong,ZHANG Dingguo,ZHU Xiangyang. Developing non-somatotopic phantom finger sensation to comparable levels of somatotopic sensation through user training with electrotactile stimulation[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2016,25(5):469-480.
[18] SU Shiyong,CHAI Guohong,SHU Xiaokang,et al. Electrical stimulation-induced SSSEP as an objective index to evaluate the difference of tactile acuity between the left and right hand[J]. Journal of Neural Engineering,2020,17(1):016053.
[19] SU Shiyong,CHAI Guohong,SHENG Xinjun,et al. Contra-lateral desynchronized alpha oscillations linearly correlate with discrimination performance of tactile acuity[J]. Journal of Neural Engineering,2020,17(4):046041.
[20] CHEN Dapeng,SONG Aiguo,TIAN Lei,et al. Development of a multidirectional controlled small-scale spherical MR actuator for haptic applications[J]. IEEE/ASME Transactions on Mechatronics,2019,24(4):1597-1607.
[21] CHEN Dapeng,SONG Aiguo,TIAN Lei,et al. FW-touch:A finger wearable haptic interface with an MR foam actuator for displaying surface material properties on a touch screen[J]. IEEE Transactions on Haptics,2019,12(3):281-294.
[22] QIN Huanhuan,SONG Aiguo,GAO Zhan,et al. A multi-finger interface with MR actuators for haptic applications[J]. IEEE Transactions on Haptics,2018,11(1):5-14.
[23] ZHOU Tiancheng,XIONG Caihua,ZHANG Juanjuan,et al. Reducing the metabolic energy of walking and running using an unpowered hip exoskeleton[J]. Journal of NeuroEngineering and Rehabilitation,2021,18(1).
[24] FENG Yanggang,WANG Qining. Combining push-off power and nonlinear damping behaviors for a lightweight motor-driven transtibial prosthesis[J]. IEEE/ASME Transactions on Mechatronics,2017,22(6):2512-2523.
[25] FENG Yanggang,MAI Jingeng,AGRAWAL S,et al. Energy regeneration from electromagnetic induction by human dynamics for lower-extremity robotic prostheses[J]. IEEE Transactions on Robotics,2020,36(5):1442-1451.
[26] GAO Siyuan,MAI Jingeng,ZHU Jinying,et al. Mechanism and controller design of a transfemoral prosthesis with electrohydraulic knee and motor-driven ankle[J]. IEEE/ASME Transactions on Mechatronics,2021,26(5):2429-2439.
[27] FAN Jun,XIONG Caihua,HUANG Zhongkui,et al. A lightweight biomechanical energy harvester with high power density and low metabolic cost[J]. Energy Conversion and Management,2019,195:641-649.
[28] YI Wei,DI Bin,LI Ruihao,et al. An actor-based programming framework for swarm robotic systems[C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems,Las Vegas,United States:2020:8012-8019.
[29] 戴华东,易晓东,王彦臻,等. 可持续自主学习的micROS机器人操作系统平行学习架构[J]. 计算机研究与发展,2019,56(1):49-57. DAI Huadong,YI Xiaodong,WANG Yanzhen,et al. Parallel learning architecture of micROS powering the ability of life-long autonomous learning[J]. Journal of Computer Research and Development,2019,56(1):49-57.
[30] RICOTTI L,TRIMMER B,FEINBERG A W,et al. Biohybrid actuators for robotics:A review of devices actuated by living cells[J]. Science Robotics,2017,2(12):eaaq0495.
[31] LASCHI C,MAZZOLAI B,CIANCHETTI M. Soft robotics:technologies and systems pushing the boundaries of robot abilities[J]. Science Robotics,2016,1(1):eaah3690.
[32] DU Lan,XU Zhiyuan,HUANG Caili,et al. From a body temperature-triggered reversible shape-memory material to high-sensitive bionic soft actuators[J]. Applied Materials Today,2019,18:100463.
[33] KANAAN A F,PIEDADE A P,DE SOUSA H C,et al. Effect of mold assemblies-induced interfaces in the mechanical actuation of electro-responsive ionic liquid-based polycationic hydrogels[J]. Applied Materials Today,2020,20:100711.
[34] GAO Peng,LI Jiahui,SHI Qiuwei. A hollow polyethylene fiber-based artificial muscle[J]. Advanced Fiber Materials,2019,1(3-4):214-221.
[35] KEPLINGER C,LI Tiefeng,BAUMGARTNER R,et al. Harnessing snap-through instability in soft dielectrics to achieve giant voltage-triggered deformation[J]. Soft Matter,2012,8:285-288.
[36] NEWMAN E A,HARTLINE P H. The infrared "vision" of snakes[J]. Scientific American,1982,246:116-127.
[37] PANZANO V C,KANG K,GARRITY P A. Infrared snake eyes:TRPA1 and the thermal sensitivity of the snake pit organ[J]. Science Signaling,2010,3(127):pe22.
[38] TSUTOMU M,KOICHI K. Image sensing and processing by a bacteriorhodopsin-based artificial photoreceptor[J]. Applied Optics,1993,32(31):6371-6379.
[39] TSUTOMU M,KOICHI K,ISAMU I. Quantum conversion and image detection by a bacteriorhodopsin- based artificial photoreceptor[J]. Science,1992,255(5042):42-344.
[40] YOSHIKO O S,TOKIMASA T,TAKAYUKI M,et al. Directionally selective motion detection with bacteriorhodopsin patterned sensor[J]. Synthetic Metals,2016,222:249-254.
[41] LI Guorui,CHEN Xiangping,ZHOU Fanghao,et al. Self-powered soft robot in the Mariana Trench[J]. Nature,2021,591:66-71.
[42] ZHUO Shuyun,ZHAO Ziguang,XIE Zhexin,et al. Complex multiphase organohydrogels with programmable mechanics toward adaptive soft-matter machines[J]. Science Advances,2020,6(5):eaax1464.
[43] XIANG Yaolei,HUANG Shenglin,HUANG Tianyun,et al. Superrepellency of underwater hierarchical structures on Salvinia leaf[J]. PNAS,2020,117(5):2282-2287.
[44] CHU Hetao,HU Xinghao,WANG Zhong,et al. Unipolar stroke,electroosmotic pump carbon nanotube yarn muscles[J]. Science,2021,371(6528):494-498.
[45] XING Zhiguang,ZHANG Junming,DAVID M,et al. A super-lightweight and soft manipulator driven by dielectric elastomers[J]. Soft Robotics,2018,7(4):512-520.
[46] LIU Lianqing,ZHANG Cuang,WANG Wenxue,et al. Regulation of C2C12 differentiation and control of the beating dynamics of contractile cells for a muscle-driven biosyncretic crawler by electrical stimulation[J]. Soft Robotics,2018,5(6):748-760.
[47] ZHANG Peng,CHEN Yucheng,LI Yuxia,et al. Flexible piezoresistive sensor with the microarray structure based on self-assembly of multi-walled carbon nanotubes[J]. Sensors,2019,19(22):4985.
[48] YAO Kun,LIN Qijing,JIANG Zhuangde,et al. Design and analysis of a combined FBG sensor for the measurement of three parameters[J]. IEEE Transactions on Instrumentation and Measurement,2021,70:1-10.
[49] MA Yuqian,BAO Jin,ZHANG Yuanwei,et al. Mammalian near-infrared image vision through injectable and self-powered retinal nanoantennae[J]. Cell,2019,177(2):243-255.
[50] LI Gongxin,WANG Feifei,YANG Wenguang,et al. Imaging with optogenetically engineered living cells as a photodetector[J]. Advanced Biosystems,2019,3(8):1800319.