Current trends in the development of intelligent unmanned autonomous systems

  • Tao Zhang
  • Qing Li
  • Chang-shui Zhang
  • Hua-wei Liang
  • Ping Li
  • Tian-miao Wang
  • Shuo Li
  • Yun-long Zhu
  • Cheng Wu


Intelligent unmanned autonomous systems are some of the most important applications of artificial intelligence (AI). The development of such systems can significantly promote innovation in AI technologies. This paper introduces the trends in the development of intelligent unmanned autonomous systems by summarizing the main achievements in each technological platform. Furthermore, we classify the relevant technologies into seven areas, including AI technologies, unmanned vehicles, unmanned aerial vehicles, service robots, space robots, marine robots, and unmanned workshops/intelligent plants. Current trends and developments in each area are introduced.

Key words

Intelligent unmanned autonomous system Autonomous vehicle Artificial intelligence Robotics Development trend 

CLC number



Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Abadi, M., Agarwal, A., Barham, P., et al., 2016. TensorFlow: large-scale machine learning on heterogeneous systems. arXiv:1603.04467.Google Scholar
  2. Akin, D.L., Bowden, M.L., 2003. Human-robotic hybrids for deep space EVA: the space construction and orbital utility transport concept. AIAA Space, p.1–11.Google Scholar
  3. Albu-Schaffer, A., Bertleff, W., Rebele, B., et al., 2006. ROKVISS-robotics component verification on ISS current experimental results on parameter identification. IEEE Int. Conf. on Robotics and Automation, p.3879–3885. Google Scholar
  4. ARC Advisory Group, 2002. Collaborative Manufacturing Management Strategies. Google Scholar
  5. Bacha, A., Bauman, C., Faruque, R., et al., 2008. Odin: Team VictorTango’s entry in the DARPA urban challenge. J. Field Robot., 25(8)):467–492. CrossRefGoogle Scholar
  6. Barkmeyer, E.J., Christopher, N., Feng, S.C., et al., 1996. SIMA Reference Architecture Part 1: Activity Models. NISTIR 5939. National Institute of Standards and Technology, Gaithersburg.CrossRefGoogle Scholar
  7. Brockman, G., Cheung, V., Pettersson, L., et al., 2016. OpenAI Gym. arXiv:1606.01540.Google Scholar
  8. Canis, B., 2015. Unmanned aircraft systems (UAS): commercial outlook for a new industry. Congressional Research Service, 7–5700.Google Scholar
  9. Chao, H.Y., Cao, Y.C., Chen, Y.Q., 2010. Autopilots for small unmanned aerial vehicles: a survey. Int. J. Contr. Automat. Syst., 8(1)):36–44. CrossRefGoogle Scholar
  10. Chase, M.S., Gunness, K., Morris, L.J., et al., 2015. Emerging trends in China’s development of unmanned systems. Research Reports: RR-990-OSD, RAND Corp., Santa Monica. Available from Google Scholar
  11. Chetlur, S., Woolley, C., Vandermersch, P., et al., 2014. cuDNN: efficient primitives for deep learning. arXiv: 1410.0759.Google Scholar
  12. Cusumano, F., Lampariello, R., Hirzinger, G., 2004. Development of tele-operation control for a free-floating robot during the grasping of a tumbling target. Inte. Conf. on Intelligent Manipulation and Grasping, p.1–6.Google Scholar
  13. Debus, T.J., Dougherty, S.P., 2009. Overview and performance of the front-end robotics enabling near-term demonstration (FREND) robotic arm. AIAA Infotech @Aerospace Conf., p.1–12. Google Scholar
  14. DIN, 2016. German standardization roadmap industry 4.0, Version 2.
  15. Fang, Z., Yang, S.C., Jain, S., et al., 2017. Robust autonomous flight in constrained and visually degraded shipboard environments. J. Field Robot., 34(1)):25–52. CrossRefGoogle Scholar
  16. Feng, W.W., 2013. Intelligent remote control car Anki Drive. Available from (in Chinese).Google Scholar
  17. Flores-Abad, A., Ma, O., Pham, K., 2013. A review of robotics technologies for on-orbit services. ADA576377, Defense Technical Information Center, Fort Belvoir.Google Scholar
  18. Funahashi, K., Nakamura, Y., 1993. Approximation of dynamical systems by continuous time recurrent neural networks. Neur. Netw., 6(6)):801–806. CrossRefGoogle Scholar
  19. Girshick, R., 2015. Fast R-CNN. IEEE Int. Conf. on Computer Vision, p.1440–1448. Google Scholar
  20. Girshick, R., Donahue, J., Darrell, T., et al., 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. IEEE Conf. on Computer Vision and Pattern Recognition, p.580–587. Google Scholar
  21. Graves, A., Mohamed, A., Hinton, G.E., 2013. Speech recognition with deep recurrent neural networks. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, p.6645–6649. Google Scholar
  22. Guizzo, E., 2011. How Google’s self-driving car works. IEEE Spectrum Online.Google Scholar
  23. Gupta, S.G., Ghonge, M.M., Jawandhiya, P.M., 2013. Review of unmanned aircraft system (UAS). Int. J. Adv. Res. Comput. Eng. Technol., 2:1646–1658.Google Scholar
  24. Harris, S., 2012. Out of the Loop: the human free future of unmanned aerial vehicles. Hoover Institution, Stanford University, USA.Google Scholar
  25. Heinrich, J., Silver, D., 2016. Deep reinforcement learning from self-play in imperfect information games. arXiv: 1603.01121.Google Scholar
  26. Hirzinger, G., Brunner, B., Dietrich, J., et al., 1994. ROTEX-the first remotely controlled robot in space. IEEE Int. Conf. on Robotics and Automation, p.2604–2611. Google Scholar
  27. Hoc, J.M., 2000. From human-machine interaction to human-machine cooperation. Ergonomics, 43(7)):833–843. CrossRefGoogle Scholar
  28. Hochreiter, S., Schmidhuber, J., 1997. Long short-term memory. Neur. Comput., 9(8)):1735–1780. CrossRefGoogle Scholar
  29. Hong, Y.B., Sun, R.C., Lin, R., et al., 2014. Mopping module design and experiments of a multifunction floor cleaning robot. 11th World Congress on Intelligent Control and Automation, p.5097–5102. Google Scholar
  30. Hsu, K., Murray, C., Cook, J., et al., 2013. China’s military unmanned aerial vehicle industry. US-China Economic and Security Review Commission, Washington D.C.Google Scholar
  31. Hu, M.H., Liu, J.H., Chen, D.S., et al., 2013. Multifunctional nursing bed with bed and chair integration-the dream of living in bed for the elderly. Technol. Appl. Robot., 2013(2)):42–46 (in Chinese).Google Scholar
  32. Huang, P.S., He, X.D., Gao, J.F., et al., 2013. Learning deep structured semantic models for web search using click-through data. Proc. 22nd ACM Int. Conf. on Information & Knowledge Management, p.2333–2338. Google Scholar
  33. Huang, W.L., Wen, D., Geng, J., et al., 2014. Task-specific performance evaluation of UGVs: case studies at the IVFC. IEEE Trans. Intell. Transp. Syst., 15(5)):1969–1979. CrossRefGoogle Scholar
  34. Huang, Y., Wu, J., Liu, C.M., et al., 2010. Overview and key technologies of autonomous vehicles. Ordn. Ind. Autom., 29(11)):8–13 (in Chinese). Google Scholar
  35. Hubel, D.H., Wiesel, T.N., 1962. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol., 160(1)):106–154. CrossRefGoogle Scholar
  36. Jia, Y.Q., Shelhamer, E., Donahue, J., et al., 2014. Caffe: convolutional architecture for fast feature embedding. Proc. 22nd ACM Int. Conf. on Multimedia, p.675–678. Google Scholar
  37. Kandaswamy, I., Xia, T., Kazanzides, P., 2014. Strategies and models for cutting satellite insulation in telerobotic servicing missions. IEEE Haptics Symp., p.467–472. Google Scholar
  38. Kendoul, F., 2012. Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems. J. Field Robot., 29(2)):315–378. CrossRefGoogle Scholar
  39. Krizhevsky, A., Sutskever, I., Hinton, G.E., 2012. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, p.1097–1105.Google Scholar
  40. Kuiken, T.A., Li, G.L., Lock, B.A., et al., 2009. Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. JAMA, 301(6)):619–628. CrossRefGoogle Scholar
  41. Landzettel, K., Preusche, C., Albu-Schaffer, A., et al., 2006. Robotic on-orbit servicing-DLR’s experience and perspective. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.4587–4594. Google Scholar
  42. LeCun, Y., Bengio, Y., 1995. Convolutional networks for images, speech, and time series. In: Arbib, M.A. (Ed.) The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge, p.255–258.Google Scholar
  43. LeCun, Y., Bengio, Y., Hinton, G., 2015. Deep learning. Nature, 521(7553)):436–444. CrossRefGoogle Scholar
  44. Lillicrap, T.P., Hunt, J.J., Pritzel, A., et al., 2015. Continuous control with deep reinforcement learning. arXiv:1509. 02971.Google Scholar
  45. Long, A.M., Richards, M.G., Hasting, D.E., 2007. On-orbit servicing: a new value proposition for satellite design and operation. J. Spacecr. Rock., 44(4)):964–976. CrossRefGoogle Scholar
  46. Lu, Y., Morris, K.C., Frechette, S., 2016. Current Standards Landscape for Smart Manufacturing Systems. NISTIR 8107, National Institute of Standards and Technology, Gaithersburg. Google Scholar
  47. Luong, M.T., Pham, H., Manning, C.D., 2015. Effective approaches to attention based neural machine translation. arXiv:1508.04025.Google Scholar
  48. Ma, L., Xue, J.R., Kawabata, K., et al., 2015. Efficient sampling-based motion planning for on-road autonomous driving. IEEE Trans. Intell. Transp. Syst., 16(4)):1–16. CrossRefGoogle Scholar
  49. Markoff, J., 2010. Google cars drive themselves. New York Times.Google Scholar
  50. Martinez, R.V., Fish, C.R., Chen, X., et al., 2012. Elastomeric origami: programmable paper-elastomer composites as pneumatic actuators. Adv. Funct. Mater., 22(7)):1376–1384. CrossRefGoogle Scholar
  51. Masanori, N., Chikara, H., Yasuo, I., et al., 1998. Results of the manipulator flight demonstration (MFD) flight operation. spaceOp98, p.1–7.Google Scholar
  52. Maza, I., Kondak, K., Bernard, M., et al., 2010. Multi-UAV cooperation and control for load transportation and deployment. J. Intell. Robot. Syst., 57:417–449. CrossRefMATHGoogle Scholar
  53. Merino, L., Caballero, F., Martínez-de Dios, J.R., et al., 2006. A cooperative perception system for multiple UAVs: application to automatic detection of forest fires. J. Field Robot., 23(3-4)):165–184. CrossRefGoogle Scholar
  54. Mikolov, T., Karafiát, M., Burget, L., et al., 2010. Recurrent neural network based language model. INTERSPEECH, p.1045–1048.Google Scholar
  55. Ministry of Industry and Information Technology of China (MIIT), Standardization Administration of China (SAC), 2015. National Smart Manufacturing Standards Architecture Construction Guidance (in Chinese).Google Scholar
  56. Mnih, V., Kavukcuoglu, K., Silver, D., et al., 2013. Playing Atari with deep reinforcement learning. arXiv:1312.5602.Google Scholar
  57. Mnih, V., Heess, N., Graves, A., et al., 2014. Recurrent models of visual attention. Advances in Neural Information Processing Systems, p.2204–2212.Google Scholar
  58. Montemerlo, M., Becker, J., Bhat, S., et al., 2008. Junior: the Stanford entry in the urban challenge. J. Field Robotics, 25(9)):569–597. CrossRefGoogle Scholar
  59. Nagaty, A., Saeedi, S., Thibault, C., et al., 2013. Control and navigation framework for quadrotor helicopters. J. Intell. Robot. Syst., 70(1-4)):1–12. CrossRefGoogle Scholar
  60. Obermark, J., Creamer, G., Kelm, B.E., et al., 2007. SUMO/FREND: vision system for autonomous satellite grapple. SPIE, 6555:65550Y. Google Scholar
  61. Oda, M., Inaba, N., Fukushima, Y., 1999. Space robot technology experiments on NASDA’s ETS-VII satellite. Adv. Robot., 13(6-8)):335–336. CrossRefGoogle Scholar
  62. Office of the Secretary of Defense (OSD), 2002. Unmanned aerial vehicles roadmap, 2002-2027. Department of Defense.Google Scholar
  63. Office of the Secretary of Defense (OSD), 2005. Unmanned aircraft systems roadmap, 2005-2030. Department of Defense.Google Scholar
  64. Oh, J., Chockalingam, V., Singh, S., et al., 2016. Control of memory, active perception, and action in minecraft. arXiv:1605.09128.Google Scholar
  65. O’Shea, T.J., Clancy, T.C., 2016. Deep reinforcement learning radio control and signal detection with KeRLym, a Gym RL agent. arXiv:1605.09221.Google Scholar
  66. Pan, Y.H., 2016. Heading toward artificial intelligence 2.0. Engineering, 2(4)):409–413. CrossRefGoogle Scholar
  67. Preusche, C., Reintsema, D., Landzettel, K., et al., 2006. Robotics component verification on ISS ROKVISS-pre-liminary results for telepresence. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.4595–4601. Google Scholar
  68. Rathbun, D., Kragelund, S., Pongpunwattana, A., et al., 2002. An evolution based path planning algorithm for autonomous motion of a UAV through uncertain environments. 21st Digital Avionics Systems Conf., p.1–12. Google Scholar
  69. Rebsamen, B., Guan, C.T., Zhang, H.H., et al., 2010. A brain controlled wheelchair to navigate in familiar environments. IEEE Trans. Neur. Syst. Rehabil. Eng., 18(6)):590–598. CrossRefGoogle Scholar
  70. Ren, J., Gao, X.G., Zheng, J.S., et al., 2010. Mission decision-making for UAV under dynamic environment. Syst. Eng. Electron., 32(1)):100–103 (in Chinese).Google Scholar
  71. Ren, S.Q., He, K.M., Girshick, R., et al., 2015. Faster R-CNN: towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, p.91–99.Google Scholar
  72. Rensink, R.A., 2000. The dynamic representation of scenes. Vis. Cogn., 7(1-3)):17–42. CrossRefGoogle Scholar
  73. Sak, H., Senior, A., Beaufays, F., 2014. Long short-term memory recurrent neural network architectures for large scale acoustic modeling. INTERSPEECH, p.338–342.Google Scholar
  74. Sato, N., Wakabayashi, Y., 2001. JEMRMS design features and topics from testing. Proc. 6th Int. Symp. on Artificial Intelligence and Robotics & Automation in Space, p.1–7.Google Scholar
  75. Settelmeyer, E., Oesterlin, W., Hartmann, R., et al., 1997. The Experimental Servicing Satellite-ESS. Int. Symp. on Space Technology and Science, p.617–621.Google Scholar
  76. Shepherd, R.F., Ilievski, F., Choi, W., et al., 2011. Multigait soft robot. PNAS, 108(51)):20400–20403. CrossRefGoogle Scholar
  77. Shi, H., Liu, X., 2014. Assessment method for autonomous mobility of UGV in a typical battlefield environment. Acta Armament., 35(S1)):17–24 (in Chinese).MathSciNetGoogle Scholar
  78. Simonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556.Google Scholar
  79. Stieber, M.E., McKay, M., Vukovich, G., et al., 1999. Vision-based sensing and control for space robotics applications. IEEE Trans. Instrum. Meas., 48(4)):807–812. CrossRefGoogle Scholar
  80. Sullivan, B.R., Akin, D.L., 2001. A survey of serviceable spacecraft failures. AIAA Space Conf. and Exposition, p.1–8. Google Scholar
  81. Sutskever, I., Vinyals, O., Le, Q.V., 2014. Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems, p.3104–3112.Google Scholar
  82. Sutton, R.S., Barto, A.G., 1998. Reinforcement Learning: an Introduction. Volume 1. MIT Press, Cambridge.Google Scholar
  83. Szegedy, C., Liu, W., Jia, Y.Q., et al., 2015. Going deeper with convolutions. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.1–9. Google Scholar
  84. Taylor, L.W., Ramakrishnan, J., 1992. Continuum modeling of the space shuttle remote manipulator system. Proc. IEEE Conf. on Decision and Control, p.626–631. Google Scholar
  85. Theano Development Team, 2016. Theano: a Python frame-work for fast computation of mathematical expressions. arXiv:1605.02688.Google Scholar
  86. Tisdale, J., Zuwhan, K., Hedrick, J.K., 2009. Autonomous UAV path planning and estimation. IEEE Robot. Autom. Mag., 16(2)):35–42. CrossRefGoogle Scholar
  87. Urmson, C., Anhalt, J., Bagnell, D., et al., 2008. Autonomous driving in urban environments: boss and the urban challenge. J. Field Robot., 25(8)):425–466. CrossRefGoogle Scholar
  88. Valavanis, K.P., 2007. Introduction. In: Valavanis, K.P. (Ed.), Advances in Unmanned Aerial Vehicles. Springer Netherlands, Dordrecht, p.3–13. CrossRefGoogle Scholar
  89. Valavanis, K.P., Vachtsevanos, G.J., 2014. Handbook of Unmanned Aerial Vehicles. Springer Publishing Company. Google Scholar
  90. van Hasselt, H., Guez, A., Silver, D., 2015. Deep reinforcement learning with double q-learning. arXiv:1509.06461.Google Scholar
  91. Vinyals, O., Toshev, A., Bengio, S., et al., 2015. Show and tell: a neural image caption generator. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.3156–3164. Google Scholar
  92. Wang, F.Z., 2016. Google released the product of smart home: Google Home. Available from (in Chinese).Google Scholar
  93. Wang, X.H., Yadav, V., Balakrishnan, S.N., 2007. Cooperative UAV formation flying with obstacle collision avoidance. IEEE Trans. Contr. Syst. Technol., 15(4)):672–679. CrossRefGoogle Scholar
  94. Wikipedia, 2016a. Unmanned aerial vehicle. Available from
  95. Wikipedia, 2016b. Unmanned combat aerial vehicle. Availabel from
  96. Wikipedia, 2016c. Human-in-the-loop. Available from
  97. Xu, K., Ba, J., Kiros, R., et al., 2015. Show, attend and tell: Neural image caption generation with visual attention. arXiv:1502.03044.Google Scholar
  98. Yim, M., Shen, W.M., Salemi, B., et al., 2007. Modular self-reconfigurable robot systems. IEEE Robot. Autom. Mag., 14(1)):43–52. CrossRefGoogle Scholar
  99. Zhang, D.D., 2016. America upgraded the death UAV to increase the life time and strengthen operational capability. Available from (in Chinese).Google Scholar
  100. Zhao, P., Chen, J.J., Song, Y., et al., 2012. Design of a control system for an autonomous vehicle based on adaptive-PID. Int. J. Adv. Robot. Syst., 9(2)):559–568. CrossRefGoogle Scholar
  101. Zimpfer, D., Spehar, P., 1996. STS-71 Shuttle/Mir GNC mission overview. Adv. Astronaut. Sci., 93:441–460.Google Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Tao Zhang
    • 1
  • Qing Li
    • 1
  • Chang-shui Zhang
    • 1
  • Hua-wei Liang
    • 2
  • Ping Li
    • 3
  • Tian-miao Wang
    • 4
  • Shuo Li
    • 5
  • Yun-long Zhu
    • 5
  • Cheng Wu
    • 1
  1. 1.Department of AutomationTsinghua UniversityBeijingChina
  2. 2.Hefei Institute of Physical ScienceChinese Academy of SciencesHefeiChina
  3. 3.School of Control Science and EngineeringZhejiang UniversityHangzhouChina
  4. 4.Robotics InstituteBeihang UniversityBeijingChina
  5. 5.Shenyang Institute of AutomationChinese Academy of SciencesShenyangChina

Personalised recommendations