Mobile-robotic machining for large complex components: A review study

  • Bo TaoEmail author
  • XingWei ZhaoEmail author
  • Han Ding


Even though the robotic machining has achieved great success in machining of small components, it lacks the competence to machine large complex components, such as wind turbine blade, train carriage, and aircraft wing. In order to cope with this issue, the mobile machining robot system, which consists of a robot arm integrated with a mobile platform, is proposed to achieve the large workspace and high dexterity, and thus has the potential to machine the large complex components. However, due to the limitation of motion accuracy and structural stiffness, the current mobile-robots are hard to satisfy the high precision requirement of machining tasks. In this paper, some historical mobile-robotic machining systems are reviewed firstly, followed by some key techniques related to structure optimization, dynamics of the machining process, localization, and control techniques, which are fundamental for the structural stiffness and motion accuracy of mobile-robots. Finally, the prospect of mobile-robotic machining and the open questions are addressed.

robotic machining industrial robot mobile-robot accuracy stiffness 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Khatib O, Yokoi K, Chang K, et al. Vehicle/arm coordination and multiple mobile manipulator decentralized cooperation. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. Osaka: IEEE, 1996. 546–553Google Scholar
  2. 2.
    Khatib O, Yokoi K, Brock O, et al. Robots in human environments: Basic autonomous capabilities. Int J Robot Res, 1999, 18: 684–696CrossRefGoogle Scholar
  3. 3.
    Bischoff R. HERMES: A humanoid mobile manipulator for service tasks. In: FSR’97 International Conference on Field and Service Robots. Canberra, 1997. 1–8Google Scholar
  4. 4.
    Helms E, Schraft R D, Hagele M. Rob@work: Robot assistant in industrial environments. In: Proceedings of 11th IEEE International Workshop on Robot and Human Interactive Communication. Berlin: IEEE, 2002. 399–404CrossRefGoogle Scholar
  5. 5.
    Stopp A, Horstmann S, Kristensen S, et al. Towards interactive learning for manufacturing assistants. In: Proceedings of 10th IEEE International Workshop on Robot and Human Interactive Communication. Paris: IEEE, 2001. 338–342Google Scholar
  6. 6.
    Asfour T, Berns K, Dillmann R. The humanoid robot armar: Design and control. In: The 1st IEEE-Ras International Conference on Humanoid Robots (Humanoids 2000). Pittsburgh, 2000. 7–8Google Scholar
  7. 7.
    Siegwart R, Nourbakhsh I R, Scaramuzza D, et al. Introduction to Autonomous Mobile Robots. Cambridge: MIT Press, 2011Google Scholar
  8. 8.
    Möller C, Schmidt H C, Koch P, et al. Machining of large scaled cfrp-parts with mobile cnc-based robotic system in aerospace industry. Procedia Manuf, 2017, 14: 17–29CrossRefGoogle Scholar
  9. 9.
    Garnier S, Subrin K, Arevalo-Siles P, et al. Mobile robot stability for complextasks in naval industries. Procedia CIRP, 2018, 72: 297–302CrossRefGoogle Scholar
  10. 10.
    Tunc L T, Shaw J. Experimental study on investigation of dynamics of hexapod robot for mobile machining. Int J Adv Manuf Technol, 2016, 84: 817–830Google Scholar
  11. 11.
    Guo Y, Dong H, Ke Y. Stiffness-oriented posture optimization in robotic machining applications. Robot Com-Int Manuf, 2015, 35: 69–76CrossRefGoogle Scholar
  12. 12.
    Carbone G, Ceccarelli M. Legged robotic systems. In: Kordic V, Lazinica A, Merdan M, eds. Cutting Edge Robotics. Mammendorf: Pro Literatur Verlag, 2005. 553–576Google Scholar
  13. 13.
    Doi T, Hodoshima R, Hirose S, et al. Development of a quadruped walking robot to work on steep slopes, TITAN XI (walking motion with compensation for compliance). In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. Edmonton: IEEE, 2005. 2067–2072CrossRefGoogle Scholar
  14. 14.
    Zhuang H C, Gao H B, Deng Z Q, et al. A review of heavy-duty legged robots. Sci China Tech Sci, 2014, 57: 298–314CrossRefGoogle Scholar
  15. 15.
    Nee A Y C. Handbook of Manufacturing Engineering and Technology. Berlin: Springer, 2015Google Scholar
  16. 16.
    Pan Z, Zhang H, Zhu Z, et al. Chatter analysis of robotic machining process. J Mater Process Tech, 2006, 173: 301–309CrossRefGoogle Scholar
  17. 17.
    Abele E, Weigold M, Rothenbücher S. Modeling and identification of an industrial robot for machining applications. CIRP Ann, 2007, 56: 387–390CrossRefGoogle Scholar
  18. 18.
    Lin Y, Zhao H, Ding H. Posture optimization methodology of 6r industrial robots for machining using performance evaluation indexes. Robot Com-Int Manuf, 2017, 48: 59–72CrossRefGoogle Scholar
  19. 19.
    Koren Y, Heisel U, Jovane F, et al. Reconfigurable manufacturing systems. CIRP Ann, 1999, 48: 527–540CrossRefGoogle Scholar
  20. 20.
    Wahl J. Articulated tool head. US Patent, 6431802. 2002Google Scholar
  21. 21.
    Siciliano B. The tricept robot: Inverse kinematics, manipulability analysis and closed-loop direct kinematics algorithm. Robotica, 1999, 17: 437–445CrossRefGoogle Scholar
  22. 22.
    Tlusty J, Ziegert J, Ridgeway S. Fundamental comparison of the use of serial and parallel kinematics for machines tools. CIRP Ann, 1999, 48: 351–356CrossRefGoogle Scholar
  23. 23.
    Zhao X, Liu H, Ding H, et al. An approach for computing the transmission index of full mobility planar multiloop mechanisms. J Mech Robotics, 2017, 9: 041017CrossRefGoogle Scholar
  24. 24.
    Weck M, Staimer D. Parallel kinematic machine tools: Current state and future potentials. CIRP Ann, 2002, 51: 671–683CrossRefGoogle Scholar
  25. 25.
    Dong C, Liu H, Yue W, et al. Stiffness modeling and analysis of a novel 5-dof hybrid robot. Mech Mach Theory, 2018, 125: 80–93CrossRefGoogle Scholar
  26. 26.
    Liu H, Huang T, Chetwynd D G, et al. Stiffness modeling of parallel mechanisms at limb and joint/link levels. IEEE Trans Robot, 2017, 33: 734–741CrossRefGoogle Scholar
  27. 27.
    Axinte D A, Allen J M, Anderson R, et al. Free-leg hexapod: A novel approach of using parallel kinematic platforms for developing miniature machine tools for special purpose operations. CIRP Ann, 2011, 60: 395–398CrossRefGoogle Scholar
  28. 28.
    Wu J, Wang D, Wang L. A control strategy of a two degrees-of-freedom heavy duty parallel manipulator. J Dyn Sys Meas Control, 2015, 137: 061007CrossRefGoogle Scholar
  29. 29.
    Wu J, Yu G, Gao Y, et al. Mechatronics modeling and vibration analysis ofa 2-DOF parallel manipulator in a 5-DOF hybrid machine tool. Mech Mach Theory, 2018, 121: 430–445CrossRefGoogle Scholar
  30. 30.
    Wu J, Wang J, Wang L, et al. Dynamics and control of a planar 3-DOF parallel manipulator with actuation redundancy. Mech Mach Theory, 2009, 44: 835–849zbMATHCrossRefGoogle Scholar
  31. 31.
    Li Q, Hervé J M. 1T2R parallel mechanisms without parasitic motion. IEEE Trans Robot, 2010, 26: 401–410CrossRefGoogle Scholar
  32. 32.
    Lian B, Sun T, Song Y, et al. Stiffness analysis and experiment of a novel 5-DOF parallel kinematic machine considering gravitational effects. Int J Mach Tool Manu, 2015, 95: 82–96CrossRefGoogle Scholar
  33. 33.
    Ma Z, Poo A N, Ang Jr M H, et al. Design, simulation and implementation of a 3-PUU parallel mechanism for a macro/mini manipulator. In: IROS 2015. The Path to Success: Failures in Real Robots. Hamburg, 2015. 42–47Google Scholar
  34. 34.
    Ma Z, See H H, Hong G S, et al. Control and modeling of an end-effector in a macro-mini manipulator system for industrial applications. In: 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). Munich: IEEE, 2017. 676–681CrossRefGoogle Scholar
  35. 35.
    Lopes A, Almeida F. A force-impedance controlled industrial robot using an active robotic auxiliary device. Robot Com-Int Manuf, 2008, 24: 299–309CrossRefGoogle Scholar
  36. 36.
    Tsai M J, Chang J L, Haung J F. Development of an automatic mold polishing system. IEEE Trans Automat Sci Eng, 2005, 2: 393–397CrossRefGoogle Scholar
  37. 37.
    Xie F, Liu X J, Wang J, et al. Kinematic optimization of a five degrees-of-freedom spatial parallel mechanism with large orientational workspace. J Mech Robotics, 2017, 9: 051005CrossRefGoogle Scholar
  38. 38.
    Ozturk E, Barrios A, Sun C, et al. Robotic assisted milling for increased productivity. CIRP Ann, 2018, 67: 427–430CrossRefGoogle Scholar
  39. 39.
    Fei J, Lin B, Yan S, et al. Chatter mitigation using moving damper. J Sound Vib, 2017, 410: 49–63CrossRefGoogle Scholar
  40. 40.
    Szynkiewicz W, Zielihska T, Kasprzak W. Robotized machining of big work pieces: Localization of supporting heads. Front Mech Eng China, 2010, 5: 357–369CrossRefGoogle Scholar
  41. 41.
    de Leonardo L, Zoppi M, Xiong L, et al. SwarmItFIX: A multi-robot-based reconfigurable fixture. Ind Robot, 2013, 40: 320–328CrossRefGoogle Scholar
  42. 42.
    Sagar K, de Leonardo L, Molfino R, et al. The swarmitfix pilot. Procedia Manuf, 2017, 11: 413–422CrossRefGoogle Scholar
  43. 43.
    Bi Q, Wang X, Wu Q, et al. Fv-SVM based wall thickness error decomposition for adaptive machining of large skin parts. IEEE Trans Ind Inf, 2019, 15: 426–2434CrossRefGoogle Scholar
  44. 44.
    Slavkovic N R, Milutinovic D S, Glavonjic M M. A method for offline compensation of cutting force-induced errors in robotic machining by tool path modification. Int J Adv Manuf Technol, 2014, 70: 2083–2096CrossRefGoogle Scholar
  45. 45.
    Liu F Y, Lü T S. Stiffness of 5-axis machines with serial, parallel, and hybrid kinematics: Evaluation and comparison. CIRP Ann, 2010, 59: 409–412CrossRefGoogle Scholar
  46. 46.
    Liu F Y, Lü T S. Development of a robot system for complex surfaces polishing based on CL data. Int J Adv Manuf Technol, 2005, 26: 1132–1137CrossRefGoogle Scholar
  47. 47.
    Ren X, Kuhlenkötter B, Müller H. Simulation and verification of belt grinding with industrial robots. Int J Mach Tool Manu, 2006, 46: 708–716CrossRefGoogle Scholar
  48. 48.
    Huang H, Gong Z M, Chen X Q, et al. Robotic grinding and polishing for turbine-vane overhaul. J Mater Process Tech, 2002, 127: 140–145CrossRefGoogle Scholar
  49. 49.
    Olsson T, Haage M, Kihlman H, et al. Cost-efficient drilling using industrial robots with high-bandwidth force feedback. Robot ComInt Manuf, 2010, 26: 24–38CrossRefGoogle Scholar
  50. 50.
    Cen L, Melkote S N, Castle J, et al. A method for mode coupling chatter detection and suppression in robotic milling. J Manuf Sci Eng, 2018, 140: 081015CrossRefGoogle Scholar
  51. 51.
    Norberto Pires J, Ramming J, Rauch S, et al. Force/torque sensing applied to industrial robotic deburring. Sens Rev, 2002, 22: 232–241CrossRefGoogle Scholar
  52. 52.
    Pan R, Zhang Y, Cao C, et al. Modeling of material removal in dynamic deterministic polishing. Int J Adv Manuf Technol, 2015, 81: 1631–1642CrossRefGoogle Scholar
  53. 53.
    Yu M, An X. Study of the kinematics for the serial robot on the controllable polishing force. AMM, 2013, 454: 114–117CrossRefGoogle Scholar
  54. 54.
    Guo M, Li B, Ding Z, et al. Empirical modeling of dynamic grinding force based on process analysis. Int J Adv Manuf Technol, 2016, 86: 3395–3405CrossRefGoogle Scholar
  55. 55.
    Tian F, Li Z, Lv C, et al. Polishing pressure investigations of robot automatic polishing on curved surfaces. Int J Adv Manuf Technol, 2016, 87: 639–646CrossRefGoogle Scholar
  56. 56.
    Rafieian F, Hazel B, Liu Z. Regenerative instability of impact-cutting material removal in the grinding process performed by a flexible robot arm. Procedia CIRP, 2014, 14: 406–411CrossRefGoogle Scholar
  57. 57.
    Tahvilian A M, Hazel B, Rafieian F, et al. Force model for impact cutting grinding with a flexible robotic tool holder. Int J Adv Manuf Technol, 2016, 85: 133–147CrossRefGoogle Scholar
  58. 58.
    Chen Y, Dong F. Robot machining: Recent development and future research issues. Int J Adv Manuf Technol, 2013, 66: 1489–1497CrossRefGoogle Scholar
  59. 59.
    Wang G, Dong H, Guo Y, et al. Early chatter identification of robotic boring process using measured force of dynamometer. Int J Adv Manuf Technol, 2018, 94: 1243–1252CrossRefGoogle Scholar
  60. 60.
    Dong Y, Bao X, Lu C, et al. Chatter identification using hht for boring process. In: 2013 International Conference on Optical Instruments and Technology: Optoelectronic Devices and Optical Signal Processing. Vol. 9043. Beijing, 2013. 904316Google Scholar
  61. 61.
    Garnier S, Subrin K, Waiyagan K. Modelling of robotic drilling. Procedia CIRP, 2017, 58: 416–421CrossRefGoogle Scholar
  62. 62.
    Miguélez M H, Rubio L, Loya J A, et al. Improvement of chatter stability in boring operations with passive vibration absorbers. Int J Mech Sci, 2010, 52: 1376–1384CrossRefGoogle Scholar
  63. 63.
    Guo Y, Dong H, Wang G, et al. Vibration analysis and suppression in robotic boring process. Int J Machi Tool Manuf, 2016, 101: 102–110CrossRefGoogle Scholar
  64. 64.
    Schneider U, Drust M, Ansaloni M, et al. Improving robotic machining accuracy through experimental error investigation and modular compensation. Int J Adv Manuf Technol, 2016, 85: 3–15CrossRefGoogle Scholar
  65. 65.
    Maurotto A, Tunc L T. Effects of chattering on surface integrity in robotic milling of alloy 690. In: ASME 2017 Pressure Vessels and Piping Conference. Hawaii: American Society of Mechanical Engineers, 2017. V06AT06A004Google Scholar
  66. 66.
    Li J, Li B, Shen N Y, et al. Effect of the cutter path and the work-piece clamping position on the stability of the robotic milling system. Int J Adv Manuf Technol, 2016, 89: 2919–2933CrossRefGoogle Scholar
  67. 67.
    Tunc L T, Stoddart D. Tool path pattern and feed direction selection in robotic milling for increased chatter-free material removal rate. Int J Adv Manuf Technol, 2017, 89: 2907–2918CrossRefGoogle Scholar
  68. 68.
    Kaldestad K B, Tyapin I, Hovland G. Robotic face milling path correction and vibration reduction. In: 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). Busan: IEEE, 2015. 543–548Google Scholar
  69. 69.
    Mousavi S, Gagnol V, Bouzgarrou B C, et al. Model-based stability prediction of a machining robot. In: Corves B, Lovasz E C, Hüsing M, et al, eds. New Advances in Mechanisms, Mechanical Transmissions and Robotics. Mechanisms and Machine Science, Vol 46. Cham: Springer, 2017. 379–387CrossRefGoogle Scholar
  70. 70.
    Endeshaw H, Alemayehu F, Ekwaro-Osire S. Reduction of chatter using a probabilistic approach. In: ASME 2015 International Mechanical Engineering Congress and Exposition. Texas: American Society of Mechanical Engineers, 2015. V02BT02A054Google Scholar
  71. 71.
    Tratar J, Pusavec F, Kopac J. Tool wear in terms of vibration effects in milling medium-density fibreboard with an industrial robot. J Mech Sci Technol, 2015, 28: 4421–4429CrossRefGoogle Scholar
  72. 72.
    Zi B, Duan B Y, Du J L, et al. Dynamic modeling and active control of a cable-suspended parallel robot. Mechatronics, 2008, 18: 1–12CrossRefGoogle Scholar
  73. 73.
    Norman A R, Schönberg A, Gorlach I A, et al. Validation of iGPS as an external measurement system for cooperative robot positioning. Int J Adv Manuf Technol, 2013, 64: 427–446CrossRefGoogle Scholar
  74. 74.
    Jiang Z H. Workspace trajectory control of flexible robot manipulators using neural network and visual sensor feedback. In: 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering. Halifax: IEEE, 2015. 1502–1507Google Scholar
  75. 75.
    Susemihl H, Moeller C, Kothe S, et al. High accuracy mobile robotic system for machining of large aircraft components. SAE Int J Aerosp, 2016, 9: 231–238CrossRefGoogle Scholar
  76. 76.
    Du H, Chen X, Zhou D, et al. Integrated fringe projection 3d scanning system for large-scale metrology based on laser tracker. In: AOPC 2017: 3D Measurement Technology for Intelligent Manufacturing. Vol. 10458. Beijing: International Society for Optics and Photonics, 2017. 104581TGoogle Scholar
  77. 77.
    Roth Z, Mooring B, Ravani B. An overview of robot calibration. IEEE J Robot Automat, 1987, 3: 377–385CrossRefGoogle Scholar
  78. 78.
    Nubiola A, Slamani M, Joubair A, et al. Comparison of two calibration methods for a small industrial robot based on an optical cmm and a laser tracker. Robotica, 2014, 32: 447–466CrossRefGoogle Scholar
  79. 79.
    Mustafa S K, Tao P Y, Yang G, et al. A geometrical approach for online error compensation of industrial manipulators. In: 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Montreal: IEEE, 2010. 738–743Google Scholar
  80. 80.
    Joubair A, Bonev I A. Non-kinematic calibration of a six-axis serial robot using planar constraints. Precision Eng, 2015, 40: 325–333CrossRefGoogle Scholar
  81. 81.
    Marie S, Courteille E, Maurine P. Elasto-geometrical modeling and calibration of robot manipulators: Application to machining and forming applications. Mech Mach Theory, 2013, 69: 13–43CrossRefGoogle Scholar
  82. 82.
    Kamali K, Joubair A, Bonev I A, et al. Elasto-geometrical calibration of an industrial robot under multidirectional external loads using a laser tracker. In: 2016 IEEE International Conference on Robotics and Automation. Stockholm: IEEE, 2016. 4320–4327Google Scholar
  83. 83.
    Borenstein J, Everett H R, Feng L. Navigating Mobile Robots: Systems and Techniques. Wellesley, MA: AK Peters, 1996zbMATHGoogle Scholar
  84. 84.
    Barshan B, Durrant-Whyte H F. Inertial navigation systems for mobile robots. IEEE Trans Robot Automat, 1995, 11: 328–342CrossRefGoogle Scholar
  85. 85.
    Borenstein J, Everett H R, Feng L, et al. Mobile robot positioning: Sensors and techniques. J Robotic Syst, 1997, 14: 231–249CrossRefGoogle Scholar
  86. 86.
    Piaggio M, Sgorbissa A, Zaccaria R. Navigation and localization for service mobile robots based on active beacons. Syst Sci, 2001, 27: 71–83Google Scholar
  87. 87.
    Lazanas A, Latombe J C. Landmark-based robot navigation. Algorithmica, 1995, 13: 472–501MathSciNetzbMATHCrossRefGoogle Scholar
  88. 88.
    Durrant-Whyte H, Bailey T. Simultaneous localization and mapping: Part I. IEEE Robot Automat Mag, 2006, 13: 99–110CrossRefGoogle Scholar
  89. 89.
    Fuentes-Pacheco J, Ruiz-Ascencio J, Rendön-Mancha J M. Visual simultaneous localization and mapping: A survey. Artif Intell Rev, 2015, 43: 55–81CrossRefGoogle Scholar
  90. 90.
    Yousif K, Bab-Hadiashar A, Hoseinnezhad R. An overview to visual odometry and visual SLAM: Applications to mobile robotics. Intell Ind Syst, 2015, 1: 289–311CrossRefGoogle Scholar
  91. 91.
    Jung M, Song J B. Robust mapping and localization in indoor environments. Intel Serv Robotics, 2017, 10: 55–66CrossRefGoogle Scholar
  92. 92.
    Kim A, Eustice R M. Active visual slam for robotic area coverage: Theory and experiment. Int J Robot Res, 2015, 34: 457–475CrossRefGoogle Scholar
  93. 93.
    Schueftan D S, Colorado M J, Bernal I F M. Indoor mapping using slam for applications in flexible manufacturing systems. In: 2015 IEEE 2nd Colombian Conference on Automatic Control. Manizales: IEEE, 2015. 1–6Google Scholar
  94. 94.
    Kiencke U, Nielsen L. Automotive control systems: For engine, driveline, and vehicle. Meas Sci Technol, 2000, 11: 1828Google Scholar
  95. 95.
    Sciavicco L, Siciliano B. Modeling and Control of Robot Manipulators. Berlin: Springer, 2012zbMATHGoogle Scholar
  96. 96.
    Liu K, Lewis F L. Decentralized continuous robust controller for mobile robots. In: Proceedings of IEEE International Conference on Robotics and Automation. Cincinnati: IEEE, 1990. 1822–1827Google Scholar
  97. 97.
    Yamamoto Y, Yun X. Effect of the dynamic interaction on coordinated control of mobile manipulators. IEEE Trans Robot Automat, 1996, 12: 816–824CrossRefGoogle Scholar
  98. 98.
    Chen Y, Liu L, Zhang M, et al. Study on coordinated control and hardware system of a mobile manipulator. In: 2006 6th World Congress on Intelligent Control and Automation. Dalian: IEEE, 2006. 9037–9041Google Scholar
  99. 99.
    White G D, Bhatt R M, Tang C P, et al. Experimental evaluation of dynamic redundancy resolution in a nonholonomic wheeled mobile manipulator. IEEE/ASME Trans Mechatron, 2009, 14: 349–357CrossRefGoogle Scholar
  100. 100.
    Soylu S, Buckham B J, Podhorodeski R P. Redundancy resolution for underwater mobile manipulators. Ocean Eng, 2010, 37: 325–343CrossRefGoogle Scholar
  101. 101.
    Colbaugh R. Adaptive stabilization of mobile manipulators. J Robotic Syst, 1998, 15: 511–523zbMATHCrossRefGoogle Scholar
  102. 102.
    Zhong G, Kobayashi Y, Hoshino Y, et al. System modeling and tracking control of mobile manipulator subjected to dynamic interaction and uncertainty. Nonlinear Dyn, 2013, 73: 167–182MathSciNetzbMATHCrossRefGoogle Scholar
  103. 103.
    Peng J, Yu J, Wang J. Robust adaptive tracking control for non-holonomic mobile manipulator with uncertainties. ISA Trans, 2014, 53: 1035–1043CrossRefGoogle Scholar
  104. 104.
    Qiao H, Wang M, Su J, et al. The concept of “attractive region in environment” and its application in high-precision tasks with low-precision systems. IEEE/ASME Trans Mechatron, 2015, 20: 2311–2327CrossRefGoogle Scholar
  105. 105.
    Qiao H. Attractive regions formed by the environment in configuration space: The possibility of achieving high precision sensorless manipulation in production. Int J Prod Res, 2002, 40: 975–1002CrossRefGoogle Scholar
  106. 106.
    Qiao H. Two- and three-dimensional part orientation by sensor-less grasping and pushing actions: Use of the concept of ‘attractive region in environment’. Int J Prod Res, 2003, 41: 3159–3184CrossRefGoogle Scholar
  107. 107.
    Sugar T, Kumar V. Decentralized control of cooperating mobile manipulators. In: Proceedings of 1998 IEEE International Conference on Robotics and Automation. Leuven: IEEE, 1998. 2916–2921Google Scholar
  108. 108.
    Yan J, Zhao J, Yang C, et al. Design of a coordinated control strategy for multi-mobile-manipulator cooperative teleoperation system. In: 2012 IEEE International Conference on Mechatronics and Automation. Chengdu: IEEE, 2012. 783–788Google Scholar
  109. 109.
    Hokayem P F, Spong M W. Bilateral teleoperation: An historical survey. Automatica, 2006, 42: 2035–2057MathSciNetzbMATHCrossRefGoogle Scholar
  110. 110.
    Ma L, Schilling K. Survey on Bilateral Teleoperation of Mobile Robots. Würzburg: ACTA Press, 2007. 489–494Google Scholar
  111. 111.
    Zhai D H, Xia Y. Adaptive fuzzy control of multilateral asymmetric teleoperation for coordinated multiple mobile manipulators. IEEE Trans Fuzzy Syst, 2016, 24: 57–70CrossRefGoogle Scholar
  112. 112.
    Elhajj I, Xi N, Fung W, et al. Modeling and control of internet based cooperative teleoperation. In: Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation. Seoul: IEEE, 2011. 662–667Google Scholar
  113. 113.
    Farkhatdinov I, Ryu J H. Teleoperation of multi-robot and multi-property systems. In: 2008 6th IEEE International Conference on Industrial Informatics. Daejeon: IEEE, 2008. 1453–1458Google Scholar
  114. 114.
    Li Z, Su C Y. Neural-adaptive control of single-master–multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties. IEEE Trans Neural Netw Learning Syst, 2013, 24: 1400–1413CrossRefGoogle Scholar
  115. 115.
    Yang X, Hua C C, Yan J, et al. Adaptive formation control of cooperative teleoperators with intermittent communications. IEEE Trans Cybern, 2019, 49: 2514–2523CrossRefGoogle Scholar
  116. 116.
    Peng Z, Yang S, Wen G, et al. Adaptive distributed formation control for multiple nonholonomic wheeled mobile robots. Neurocomputing, 2016, 173: 1485–1494CrossRefGoogle Scholar
  117. 117.
    Du H, Wen G, Cheng Y, et al. Distributed finite-time cooperative control of multiple high-order nonholonomic mobile robots. IEEE Trans Neural Netw Learning Syst, 2017, 28: 2998–3006MathSciNetCrossRefGoogle Scholar
  118. 118.
    Kantaros Y, Zavlanos M M. Distributed intermittent connectivity control of mobile robot networks. IEEE Trans Automat Contr, 2017, 62: 3109–3121MathSciNetzbMATHCrossRefGoogle Scholar
  119. 119.
    Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning. Nature, 2015, 518: 529–533CrossRefGoogle Scholar
  120. 120.
    Arulkumaran K, Deisenroth M P, Brundage M, et al. Deep reinforcement learning: A brief survey. IEEE Signal Process Mag, 2017, 34: 26–38CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhanChina

Personalised recommendations