智能机器人研究现状及发展趋势的思考与建议
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摘要
随着工业化进程的推进和信息化时代的到来,智能机器人在智能制造、智能交通自动化、物联网、医疗健康与智能服务等方面扮演越来越重要的角色。本文结合作者在智能机器人领域的相关工作,分析国内外智能机器人发展研究的基础上,就目前人机协作、无人驾驶、情感识别、脑机接口、仿生软体机器人和云平台、大数据网络等关键与前沿技术的研究作简要的综述,概要展望了其发展趋势并提出关于我国智能机器人发展的思考与建议。希望能够在把握国内外智能机器人前沿技术发展的同时,为发展我国智能机器人技术与产业提供相关理论、方法及技术方面的参考与借鉴。
With the advancing of industrialization and the advent of the information age, intelligent robots play an increasingly important role in intelligent manufacturing, intelligent transportation system, Internet of things, medical health and intelligent services. Based on working experiences and reviews on intelligent robot studies both in China and abroad, the authors summarize researches on key and leading technologies related to human-robot collaboration, driverless technology, emotion recognition, brain-computer interface, bionic software robot and cloud platform, big data network, etc. The development trend of intelligent robot is discussed, and some insights and suggestions on intelligent robot development in China are proposed. The review is not only aimed to overview leading technologies of intelligent robot all over the world, but also provide related theories, methods and technical guidance to the technological and industrial development of intelligent robot in China.
With the advancing of industrialization and the advent of the information age, intelligent robots play an increasingly important role in intelligent manufacturing, intelligent transportation system, Internet of things, medical health and intelligent services. Based on working experiences and reviews on intelligent robot studies both in China and abroad, the authors summarize researches on key and leading technologies related to human-robot collaboration, driverless technology, emotion recognition, brain-computer interface, bionic software robot and cloud platform, big data network, etc. The development trend of intelligent robot is discussed, and some insights and suggestions on intelligent robot development in China are proposed. The review is not only aimed to overview leading technologies of intelligent robot all over the world, but also provide related theories, methods and technical guidance to the technological and industrial development of intelligent robot in China.
引文
[ 1] Wang T M, Tao Y, Liu H. Current researches and future development trend of intelligent robot: a review[J]. International Journal of Automation & Computing, 2018(9):1-22
[ 2] International Federation of Robotics. Industrial robotics standardization[EB/OL]. http://www.ifr.org/news/ifr-press-release/iso-robotics standardisation-35/:IFR, 2018
[ 3] International Federation of Robotics. Industrial robot as defined by ISO 8373[EB/OL]. http://www.ifr.org/industrial-robots:IFR, 2018
[ 4] International Federation of Robotics. Service robots[EB/OL]. http://www.ifr.org/service-robot/:IFR, 2018
[ 5] 王伟同. 中国人口红利的经济增长“尾效”研究——兼论刘易斯拐点后的中国经济[J]. 财贸经济, 2012(11):14-20
[ 6] 鲁棒. 2011年全球机器人市场进入快车道[J]. 机器人技术与应用, 2012(4):10-11
[ 7] DIE智库. 2017年机器人产业数据概览[EB/OL]. http://www.sohu.com/a/217838008765932: sohu, 2018
[ 8] 工业机器人之家. 2017年全球工业机器人市场分析报告[EB/OL]. http://mini.eastday.com/mobile/171124104639463.html:东方网,2017
[ 9] Lan Z C. 政务服务机器人发展现状调查:一体化解决方案是大势所趋[EB/OL]. https://blog.csdn.net/lanzhichen/article/details/80938376:lanzhichen, 2018
[10] Tanaka F, Isshiki K, Takahashi F, et al. Pepper learns together with children: Development of an educational application. In: Proceedings of the IEEE-RAS 15th International Conference on Humanoid Robots, Seoul, Korea, 2015. 270-275
[11] Metta G, Sandini G, Vernon D, et al. The iCub humanoid robot: an open platform for research in embodied cognition[C]. In: Proceedings of the Workshop on Performance Metrics for Intelligent Systems, Gaithersburg, USA, 2008. 50-56
[12] Tian Z M, Lu W S, Wang T M. Application of a robotic telemanipulation system in stereotactic surgery[J]. Stereotact Funct Neurosurg, 2008, 86: 54-61
[13] 王树新, 王晓菲, 张建勋, 等. 辅助腹腔微创手术的新型机器人“妙手A”[J]. 机器人技术与应用, 2011(4): 17-21
[14] Kuindersma S, Deits R, Fallon M, et al. Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot[J]. Autonomous Robots, 2016, 40(3):429-455
[15] 林兆花, 徐天亮. 机器人技术在物流业中的应用[J]. 物流技术, 2012, 31(13):42-45
[16] International Organization for Standardization.15066 Robots and robotic devices:Collaborative robots[S]. International Organization for Standardization, 2016
[17] Wolf S, Hirzinger G. A new variable stiffness design: Matching requirements of the next robot generation[C]. In: Proceedings of the IEEE International Conference on Robotics and Automation, Pasadena, USA, 2008.1741-1746
[18] Choi J, Hong S, Lee W, et al. A robot joint with variable stiffness using leaf springs[J]. IEEE Transactions on Robotics, 2011, 27(2):229-238
[19] Wolf S, Eiberger O, Hirzinger G. The DLR FSJ: energy based design of a variable stiffness joint[C]. In: Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, China, 2011. 5082-5089
[20] Zanchettin A M, Ceriani N M, Rocco P, et al. Safety in human-robot collaborative manufacturing environments: Metrics and control[J]. IEEE Transactions on Automation Science & Engineering, 2015, 13(2):882-893
[21] Zinn M, Khatib O, Roth B. A new actuation approach for human friendly robot design[C]. In: Proceedings of the IEEE International Conference on Robotics and Automation, New Orleans, USA, 2004. 249-254
[22] Gutmann J S, Fukuchi M, Fujita M. 3D perception and environment map generation for humanoid robot navigation[J]. International Journal of Robotics Research, 2008, 27(27):1117-1134
[23] Schmitz A, Maiolino P, Maggiali M, et al. Methods and technologies for the implementation of large-scale robot tactile sensors[J]. IEEE Transactions on Robotics, 2011, 27(3):389-400
[24] Fanaei A, Farrokhi M. Robust adaptive neuro-fuzzy controller for hybrid position/force control of robot manipulators in contact with unknown environment[J]. Journal of Intelligent & Fuzzy Systems, 2006, 17(17):125-144
[25] Masuta H, Kubota N. Information reduction for environment perception of an intelligent robot arm equipped with a 3D range camera[C]. In: Proceedings of IEEE Sice Conference, Taipei, China, 2010. 392-397
[26] Davison A J, Reid I D, Molton N D, et al. MonoSLAM: real-time single camera SLAM[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2007, 29(6):1052
[27] Bl?sch M, Weiss S, Scaramuzza D, et al. Vision based MAV navigation in unknown and unstructured environments[C]. In: Proceedings of the IEEE International Conference on Robotics and Automation, Anchorage, USA, 2010. 21-28
[28] 刘智勇. 智能交通控制理论及其应用[M]. 北京:科学出版社, 2003
[29] Bojarski M, Del Testa D, Dworakowski D, et al. End to end learning for self-driving cars[J]. 2016, arXiv:1604.07316
[30] Olfati-Saber R, Fax J A, Murray R M. Consensus and cooperation in networked multi-agent systems[J]. Proceedings of the IEEE, 2007, 95(1):215-233
[31] Shi G, Johansson K H. Multi-agent robust consensus: convergence analysis[C]. In: Proceedings of the IEEE Decision and Control and European Control Conference, Orlando, USA, 2011. 5744-5749
[32] Sun Y G, Wang L, Xie G. Average consensus in networks of dynamic agents with switching topologies and multiple time-varying delays[J]. Systems & Control Letters, 2008, 57(2):175-183
[33] Brambilla M, Ferrante E, Birattari M, et al. Swarm robotics: a review from the swarm engineering perspective[J]. Swarm Intelligence, 2013, 7(1):1-41
[34] 薛宏涛, 叶媛媛, 沈林成,等. 多智能体系统体系结构及协调机制研究综述[J]. 机器人, 2001, 23(1):85-90
[35] Flint M, Polycarpou M, Fernandez-Gaucherand E. Cooperative control for multiple autonomous UAV’s searching for targets[C]. In: Proceedings of the IEEE Conference on Decision and Control, Las Vegas, USA, 2003. 2823-2828
[36] Hafez A T, Marasco A J, Givigi S N, et al. Solving multi-UAV dynamic encirclement via model predictive control[J]. IEEE Transactions on Control Systems Technology, 2015, 23(6):2251-2265
[37] 张颖, 罗森林. 情感建模与情感识别[J]. 计算机工程与应用, 2003, 39(33):98-102
[38] Cambria E. Affective computing and sentiment analysis[J]. IEEE Intelligent Systems, 2016, 31(2):102-107
[39] Bartels A, Zeki S. The neural correlates of maternal and romantic love[J]. Neuroimage, 2004, 21(3):1155-1166
[40] Lin J, Yu H, Miao C, et al. An affective agent for studying composite emotions[C]. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, Istanbul, Turkey, 2015. 1947-1948
[41] Zeng Z, Tu J, Pianfetti B M, et al. Audio-visual affective expression recognition through multistream fused HMM[J]. IEEE Transactions on Multimedia, 2008, 10(4):570-577
[42] Happy S L, Routray A. Automatic facial expression recognition using features of salient facial patches[J]. IEEE Transactions on Affective Computing, 2015, 6(1):1-12
[43] Zeng Z, Pantic M, Roisman G I, et al. A survey of affect recognition methods: audio, visual, and spontaneous expressions[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2009, 31(1):39-58
[44] Dobson J. Remote control of cellular behaviour with magnetic nanoparticles[J]. Nature Nanotechnology, 2008, 3(3):139
[45] Hochberg L R, Bacher D, Jarosiewicz B, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm.[J]. Nature, 2013, 485(7398):372
[46] Vansteensel M J, Pels E G M, Bleichner M G, et al. Fully implanted brain: computer interface in a locked-In patient with ALS[J]. New England Journal of Medicine, 2016, 375(21):2060
[47] Ajiboye A B, Willett F R, Young D R, et al. Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration[J]. Lancet, 2017: S0140673617306013
[48] Zhu X, Goldberg A B, Brachman R, et al. Introduction to Semi-supervised Learning[M]. Morgan and Claypool Publishers, 2006. 130
[49] Englert P, Paraschos A, Deisenroth M P, et al. Probabilistic model-based imitation learning[J]. Adaptive Behavior, 2013, 21(5): 388-403
[50] Argall B D, Chernova S, Veloso M, et al. A survey of robot learning from demonstration[J]. Robotics & Autonomous Systems, 2009, 57(5):469-483
[51] Peters J. Probabilistic model-based imitation learning[J]. Adaptive Behavior, 2013, 21(5): 388-403
[52] Levine S, Pastor P, Krizhevsky A, et al. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection[J]. The International Journal of Robotics Research, 2017. doi:10.1177/0278364917710318
[53] Finn C, Levine S. Deep visual foresight for planning robot motion[J]. 2016, arXiv:1610.00696
[54] Ranzani T, Gerboni G, Cianchetti M, et al. A bioinspired soft manipulator for minimally invasive surgery[J]. Bioinspiration&Biomimetics, 2015, 10(3):035008
[55] Luo M, Tao W, Chen F, et al. Design improvements and dynamic characterization on fluidic elastomer actuators for a soft robotic snake[C]. In: Proceedings of the 2014 IEEE International Conference on Technologies for Practical Robot Applications (TePRA), Woburn, USA, 2014. 1-6
[56] Deimel R, Brock O. A Novel Type of Compliant and Underactuated Robotic Hand for Dexterous Grasping[M]. Sage Publications, Inc. 2016
[57] Rolf M, Steil J J. Constant curvature continuum kinematics as fast approximate model for the bionic handling assistant[C]. In: Proceedings of the LEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Portugal, 2012. 3440-3446
[58] Lei J, Yu H, Wang T. Dynamic bending of bionic flexible body driven by pneumatic artificial muscles(PAMs) for spinning gait of quadruped robot[J]. Chinese Journal of Mechanical Engineering, 2016, 29(1):11-20
[59] Laschi C, Cianchetti M, Mazzolai B, et al. Soft robot arm inspired by the octopus[J]. Advanced Robotics, 2012, 26(7):709-727
[60] Aukes D M, Heyneman B, Ulmen J, et al. Design and testing of a selectively compliant underactuated hand[J]. International Journal of Robotics Research, 2016, 33(5):721-735
[61] Kuffner J J, Lavalle S M. Space-filling trees: A new perspective on incremental search for motion planning[C]. In: Proceedings of the LEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, USA, 2011. 2199-2206
[62] 田国会, 许亚雄. 云机器人:概念、架构与关键技术研究综述[J]. 山东大学学报(工学版), 2014, 44(6):47-54
[63] Quigley M, Conley K, Gerkey B P, et al. ROS: an open-source robot operating system[C]. In: Proceedings of the ICRA Workshop on Open Source Software, Kobe, Japan, 2009. 1-6
[64] Yuriyama M, Kushida T. Sensor-cloud infrastructure: physical sensor management with virtualized sensors on cloud computing[C]. In: Proceedings of the International Conference on Network-based Information Systems, Takayama, Japan, 2010. 1-8
[65] Nakagawa S, Igarashi N, Tsuchiya Y, et al. An implementation of a distributed service framework for cloud-based robot services[C]. In: Proceedings of Annual Conference of the IEEE Industrial Electronics Society, Montreal, Canada, 2012. 4148-4153
[66] Turnbull L, Samanta B. Cloud robotics: Formation control of a multi robot system utilizing cloud infrastructure[C]. In: Proceedings of the Southeastcon, Jacksonville, USA, 2013. 1-4
[67] Kehoe B, Matsukawa A, Candido S, et al. Cloud-based robot grasping with the google object recognition engine[C]. In: Proceedings of the IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, 2013. 4263-4270
[68] Silver D, Huang A, Maddison C J, et al. Mastering the game of Go with deep neural networks and tree search[J]. Nature, 2016, 529(7587):484-489
[ 2] International Federation of Robotics. Industrial robotics standardization[EB/OL]. http://www.ifr.org/news/ifr-press-release/iso-robotics standardisation-35/:IFR, 2018
[ 3] International Federation of Robotics. Industrial robot as defined by ISO 8373[EB/OL]. http://www.ifr.org/industrial-robots:IFR, 2018
[ 4] International Federation of Robotics. Service robots[EB/OL]. http://www.ifr.org/service-robot/:IFR, 2018
[ 5] 王伟同. 中国人口红利的经济增长“尾效”研究——兼论刘易斯拐点后的中国经济[J]. 财贸经济, 2012(11):14-20
[ 6] 鲁棒. 2011年全球机器人市场进入快车道[J]. 机器人技术与应用, 2012(4):10-11
[ 7] DIE智库. 2017年机器人产业数据概览[EB/OL]. http://www.sohu.com/a/217838008765932: sohu, 2018
[ 8] 工业机器人之家. 2017年全球工业机器人市场分析报告[EB/OL]. http://mini.eastday.com/mobile/171124104639463.html:东方网,2017
[ 9] Lan Z C. 政务服务机器人发展现状调查:一体化解决方案是大势所趋[EB/OL]. https://blog.csdn.net/lanzhichen/article/details/80938376:lanzhichen, 2018
[10] Tanaka F, Isshiki K, Takahashi F, et al. Pepper learns together with children: Development of an educational application. In: Proceedings of the IEEE-RAS 15th International Conference on Humanoid Robots, Seoul, Korea, 2015. 270-275
[11] Metta G, Sandini G, Vernon D, et al. The iCub humanoid robot: an open platform for research in embodied cognition[C]. In: Proceedings of the Workshop on Performance Metrics for Intelligent Systems, Gaithersburg, USA, 2008. 50-56
[12] Tian Z M, Lu W S, Wang T M. Application of a robotic telemanipulation system in stereotactic surgery[J]. Stereotact Funct Neurosurg, 2008, 86: 54-61
[13] 王树新, 王晓菲, 张建勋, 等. 辅助腹腔微创手术的新型机器人“妙手A”[J]. 机器人技术与应用, 2011(4): 17-21
[14] Kuindersma S, Deits R, Fallon M, et al. Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot[J]. Autonomous Robots, 2016, 40(3):429-455
[15] 林兆花, 徐天亮. 机器人技术在物流业中的应用[J]. 物流技术, 2012, 31(13):42-45
[16] International Organization for Standardization.15066 Robots and robotic devices:Collaborative robots[S]. International Organization for Standardization, 2016
[17] Wolf S, Hirzinger G. A new variable stiffness design: Matching requirements of the next robot generation[C]. In: Proceedings of the IEEE International Conference on Robotics and Automation, Pasadena, USA, 2008.1741-1746
[18] Choi J, Hong S, Lee W, et al. A robot joint with variable stiffness using leaf springs[J]. IEEE Transactions on Robotics, 2011, 27(2):229-238
[19] Wolf S, Eiberger O, Hirzinger G. The DLR FSJ: energy based design of a variable stiffness joint[C]. In: Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, China, 2011. 5082-5089
[20] Zanchettin A M, Ceriani N M, Rocco P, et al. Safety in human-robot collaborative manufacturing environments: Metrics and control[J]. IEEE Transactions on Automation Science & Engineering, 2015, 13(2):882-893
[21] Zinn M, Khatib O, Roth B. A new actuation approach for human friendly robot design[C]. In: Proceedings of the IEEE International Conference on Robotics and Automation, New Orleans, USA, 2004. 249-254
[22] Gutmann J S, Fukuchi M, Fujita M. 3D perception and environment map generation for humanoid robot navigation[J]. International Journal of Robotics Research, 2008, 27(27):1117-1134
[23] Schmitz A, Maiolino P, Maggiali M, et al. Methods and technologies for the implementation of large-scale robot tactile sensors[J]. IEEE Transactions on Robotics, 2011, 27(3):389-400
[24] Fanaei A, Farrokhi M. Robust adaptive neuro-fuzzy controller for hybrid position/force control of robot manipulators in contact with unknown environment[J]. Journal of Intelligent & Fuzzy Systems, 2006, 17(17):125-144
[25] Masuta H, Kubota N. Information reduction for environment perception of an intelligent robot arm equipped with a 3D range camera[C]. In: Proceedings of IEEE Sice Conference, Taipei, China, 2010. 392-397
[26] Davison A J, Reid I D, Molton N D, et al. MonoSLAM: real-time single camera SLAM[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2007, 29(6):1052
[27] Bl?sch M, Weiss S, Scaramuzza D, et al. Vision based MAV navigation in unknown and unstructured environments[C]. In: Proceedings of the IEEE International Conference on Robotics and Automation, Anchorage, USA, 2010. 21-28
[28] 刘智勇. 智能交通控制理论及其应用[M]. 北京:科学出版社, 2003
[29] Bojarski M, Del Testa D, Dworakowski D, et al. End to end learning for self-driving cars[J]. 2016, arXiv:1604.07316
[30] Olfati-Saber R, Fax J A, Murray R M. Consensus and cooperation in networked multi-agent systems[J]. Proceedings of the IEEE, 2007, 95(1):215-233
[31] Shi G, Johansson K H. Multi-agent robust consensus: convergence analysis[C]. In: Proceedings of the IEEE Decision and Control and European Control Conference, Orlando, USA, 2011. 5744-5749
[32] Sun Y G, Wang L, Xie G. Average consensus in networks of dynamic agents with switching topologies and multiple time-varying delays[J]. Systems & Control Letters, 2008, 57(2):175-183
[33] Brambilla M, Ferrante E, Birattari M, et al. Swarm robotics: a review from the swarm engineering perspective[J]. Swarm Intelligence, 2013, 7(1):1-41
[34] 薛宏涛, 叶媛媛, 沈林成,等. 多智能体系统体系结构及协调机制研究综述[J]. 机器人, 2001, 23(1):85-90
[35] Flint M, Polycarpou M, Fernandez-Gaucherand E. Cooperative control for multiple autonomous UAV’s searching for targets[C]. In: Proceedings of the IEEE Conference on Decision and Control, Las Vegas, USA, 2003. 2823-2828
[36] Hafez A T, Marasco A J, Givigi S N, et al. Solving multi-UAV dynamic encirclement via model predictive control[J]. IEEE Transactions on Control Systems Technology, 2015, 23(6):2251-2265
[37] 张颖, 罗森林. 情感建模与情感识别[J]. 计算机工程与应用, 2003, 39(33):98-102
[38] Cambria E. Affective computing and sentiment analysis[J]. IEEE Intelligent Systems, 2016, 31(2):102-107
[39] Bartels A, Zeki S. The neural correlates of maternal and romantic love[J]. Neuroimage, 2004, 21(3):1155-1166
[40] Lin J, Yu H, Miao C, et al. An affective agent for studying composite emotions[C]. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, Istanbul, Turkey, 2015. 1947-1948
[41] Zeng Z, Tu J, Pianfetti B M, et al. Audio-visual affective expression recognition through multistream fused HMM[J]. IEEE Transactions on Multimedia, 2008, 10(4):570-577
[42] Happy S L, Routray A. Automatic facial expression recognition using features of salient facial patches[J]. IEEE Transactions on Affective Computing, 2015, 6(1):1-12
[43] Zeng Z, Pantic M, Roisman G I, et al. A survey of affect recognition methods: audio, visual, and spontaneous expressions[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2009, 31(1):39-58
[44] Dobson J. Remote control of cellular behaviour with magnetic nanoparticles[J]. Nature Nanotechnology, 2008, 3(3):139
[45] Hochberg L R, Bacher D, Jarosiewicz B, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm.[J]. Nature, 2013, 485(7398):372
[46] Vansteensel M J, Pels E G M, Bleichner M G, et al. Fully implanted brain: computer interface in a locked-In patient with ALS[J]. New England Journal of Medicine, 2016, 375(21):2060
[47] Ajiboye A B, Willett F R, Young D R, et al. Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration[J]. Lancet, 2017: S0140673617306013
[48] Zhu X, Goldberg A B, Brachman R, et al. Introduction to Semi-supervised Learning[M]. Morgan and Claypool Publishers, 2006. 130
[49] Englert P, Paraschos A, Deisenroth M P, et al. Probabilistic model-based imitation learning[J]. Adaptive Behavior, 2013, 21(5): 388-403
[50] Argall B D, Chernova S, Veloso M, et al. A survey of robot learning from demonstration[J]. Robotics & Autonomous Systems, 2009, 57(5):469-483
[51] Peters J. Probabilistic model-based imitation learning[J]. Adaptive Behavior, 2013, 21(5): 388-403
[52] Levine S, Pastor P, Krizhevsky A, et al. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection[J]. The International Journal of Robotics Research, 2017. doi:10.1177/0278364917710318
[53] Finn C, Levine S. Deep visual foresight for planning robot motion[J]. 2016, arXiv:1610.00696
[54] Ranzani T, Gerboni G, Cianchetti M, et al. A bioinspired soft manipulator for minimally invasive surgery[J]. Bioinspiration&Biomimetics, 2015, 10(3):035008
[55] Luo M, Tao W, Chen F, et al. Design improvements and dynamic characterization on fluidic elastomer actuators for a soft robotic snake[C]. In: Proceedings of the 2014 IEEE International Conference on Technologies for Practical Robot Applications (TePRA), Woburn, USA, 2014. 1-6
[56] Deimel R, Brock O. A Novel Type of Compliant and Underactuated Robotic Hand for Dexterous Grasping[M]. Sage Publications, Inc. 2016
[57] Rolf M, Steil J J. Constant curvature continuum kinematics as fast approximate model for the bionic handling assistant[C]. In: Proceedings of the LEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Portugal, 2012. 3440-3446
[58] Lei J, Yu H, Wang T. Dynamic bending of bionic flexible body driven by pneumatic artificial muscles(PAMs) for spinning gait of quadruped robot[J]. Chinese Journal of Mechanical Engineering, 2016, 29(1):11-20
[59] Laschi C, Cianchetti M, Mazzolai B, et al. Soft robot arm inspired by the octopus[J]. Advanced Robotics, 2012, 26(7):709-727
[60] Aukes D M, Heyneman B, Ulmen J, et al. Design and testing of a selectively compliant underactuated hand[J]. International Journal of Robotics Research, 2016, 33(5):721-735
[61] Kuffner J J, Lavalle S M. Space-filling trees: A new perspective on incremental search for motion planning[C]. In: Proceedings of the LEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, USA, 2011. 2199-2206
[62] 田国会, 许亚雄. 云机器人:概念、架构与关键技术研究综述[J]. 山东大学学报(工学版), 2014, 44(6):47-54
[63] Quigley M, Conley K, Gerkey B P, et al. ROS: an open-source robot operating system[C]. In: Proceedings of the ICRA Workshop on Open Source Software, Kobe, Japan, 2009. 1-6
[64] Yuriyama M, Kushida T. Sensor-cloud infrastructure: physical sensor management with virtualized sensors on cloud computing[C]. In: Proceedings of the International Conference on Network-based Information Systems, Takayama, Japan, 2010. 1-8
[65] Nakagawa S, Igarashi N, Tsuchiya Y, et al. An implementation of a distributed service framework for cloud-based robot services[C]. In: Proceedings of Annual Conference of the IEEE Industrial Electronics Society, Montreal, Canada, 2012. 4148-4153
[66] Turnbull L, Samanta B. Cloud robotics: Formation control of a multi robot system utilizing cloud infrastructure[C]. In: Proceedings of the Southeastcon, Jacksonville, USA, 2013. 1-4
[67] Kehoe B, Matsukawa A, Candido S, et al. Cloud-based robot grasping with the google object recognition engine[C]. In: Proceedings of the IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, 2013. 4263-4270
[68] Silver D, Huang A, Maddison C J, et al. Mastering the game of Go with deep neural networks and tree search[J]. Nature, 2016, 529(7587):484-489