CLC number: TP2
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2016-12-29
Cited: 1
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Tao Zhang, Qing Li, Chang-shui Zhang, Hua-wei Liang, Ping Li, Tian-miao Wang, Shuo Li, Yun-long Zhu, Cheng Wu. Current trends in the development of intelligent unmanned autonomous systems[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 68-85.
@article{title="Current trends in the development of intelligent unmanned autonomous systems",
author="Tao Zhang, Qing Li, Chang-shui Zhang, Hua-wei Liang, Ping Li, Tian-miao Wang, Shuo Li, Yun-long Zhu, Cheng Wu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="1",
pages="68-85",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601650"
}
%0 Journal Article
%T Current trends in the development of intelligent unmanned autonomous systems
%A Tao Zhang
%A Qing Li
%A Chang-shui Zhang
%A Hua-wei Liang
%A Ping Li
%A Tian-miao Wang
%A Shuo Li
%A Yun-long Zhu
%A Cheng Wu
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 1
%P 68-85
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601650
TY - JOUR
T1 - Current trends in the development of intelligent unmanned autonomous systems
A1 - Tao Zhang
A1 - Qing Li
A1 - Chang-shui Zhang
A1 - Hua-wei Liang
A1 - Ping Li
A1 - Tian-miao Wang
A1 - Shuo Li
A1 - Yun-long Zhu
A1 - Cheng Wu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 1
SP - 68
EP - 85
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601650
Abstract: 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.
[1]Abadi, M., Agarwal, A., Barham, P., et al., 2016. TensorFlow: large-scale machine learning on heterogeneous systems. arXiv:1603.04467.
[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.
[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.
[4]ARC Advisory Group, 2002. Collaborative Manufacturing Management Strategies. https://www.arcweb.com/
[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.
[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.
[7]Brockman, G., Cheung, V., Pettersson, L., et al., 2016. OpenAI Gym. arXiv:1606.01540.
[8]Canis, B., 2015. Unmanned aircraft systems (UAS): commercial outlook for a new industry. Congressional Research Service, 7-5700.
[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.
[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 http://www.rand.org/pubs/ research_reports/RR990.html
[11]Chetlur, S., Woolley, C., Vandermersch, P., et al., 2014. cuDNN: efficient primitives for deep learning. arXiv: 1410.0759.
[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.
[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.
[14]DIN, 2016. German standardization roadmap industry 4.0, Version 2. http://www.din.de/de
[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.
[16]Feng, W.W., 2013. Intelligent remote control car Anki Drive. Available from http://www.leiphone.com/news/201406/anki-drive-open.html (inChinese).
[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.
[18]Funahashi, K., Nakamura, Y., 1993. Approximation of dynamical systems by continuous time recurrent neural networks. Neur. Netw., 6(6):801-806.
[19]Girshick, R., 2015. Fast R-CNN. IEEE Int. Conf. on Computer Vision, p.1440-1448.
[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.
[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.
[22]Guizzo, E., 2011. How Google’s self-driving car works. IEEE Spectrum Online.
[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.
[24]Harris, S., 2012. Out of the Loop: the human free future of unmanned aerial vehicles. Hoover Institution, Stanford University, USA.
[25]Heinrich, J., Silver, D., 2016. Deep reinforcement learning from self-play in imperfect information games. arXiv: 1603.01121.
[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.
[27]Hoc, J.M., 2000. From human-machine interaction to human-machine cooperation. Ergonomics, 43(7):833-843.
[28]Hochreiter, S., Schmidhuber, J., 1997. Long short-term memory. Neur. Comput., 9(8):1735-1780.
[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.
[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.
[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).
[32]Huang, P.S., He, X.D., Gao, J.F., et al., 2013. Learning deep structured semantic models for web search using clickthrough data. Proc. 22nd ACM Int. Conf. on Information & Knowledge Management, p.2333-2338.
[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.
[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).
[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.
[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.
[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.
[38]Kendoul, F., 2012. Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems. J. Field Robot., 29(2):315-378.
[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.
[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.
[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.
[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.
[43]LeCun, Y., Bengio, Y., Hinton, G., 2015. Deep learning. Nature, 521(7553):436-444.
[44]Lillicrap, T.P., Hunt, J.J., Pritzel, A., et al., 2015. Continuous control with deep reinforcement learning. arXiv:1509. 02971.
[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.
[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.
[47]Luong, M.T., Pham, H., Manning, C.D., 2015. Effective approaches to attention based neural machine translation. arXiv:1508.04025.
[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.
[49]Markoff, J., 2010. Google cars drive themselves. New York Times.
[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.
[51]Masanori, N., Chikara, H., Yasuo, I., et al., 1998. Results of the manipulator flight demonstration (MFD) flight operation. spaceOp98, p.1-7.
[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.
[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.
[54]Mikolov, T., Karafiát, M., Burget, L., et al., 2010. Recurrent neural network based language model. INTERSPEECH, p.1045-1048.
[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).
[56]Mnih, V., Kavukcuoglu, K., Silver, D., et al., 2013. Playing Atari with deep reinforcement learning. arXiv:1312.5602.
[57]Mnih, V., Heess, N., Graves, A., et al., 2014. Recurrent models of visual attention. Advances in Neural Information Processing Systems, p.2204-2212.
[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.
[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.
[60]Obermark, J., Creamer, G., Kelm, B.E., et al., 2007. SUMO/ FREND: vision system for autonomous satellite grapple. SPIE, 6555:65550Y.
[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.
[62]Office of the Secretary of Defense (OSD), 2002. Unmanned aerial vehicles roadmap, 2002-2027. Department of Defense.
[63]Office of the Secretary of Defense (OSD), 2005. Unmanned aircraft systems roadmap, 2005-2030. Department of Defense.
[64]Oh, J., Chockalingam, V., Singh, S., et al., 2016. Control of memory, active perception, and action in minecraft. arXiv:1605.09128.
[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.
[66]Pan, Y.H., 2016. Heading toward artificial intelligence 2.0. Engineering, 2(4):409-413.
[67]Preusche, C., Reintsema, D., Landzettel, K., et al., 2006. Robotics component verification on ISS ROKVISS-preliminary results for telepresence. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.4595-4601.
[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.
[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.
[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).
[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.
[72]Rensink, R.A., 2000. The dynamic representation of scenes. Vis. Cogn., 7(1-3):17-42.
[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.
[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.
[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.
[76]Shepherd, R.F., Ilievski, F., Choi, W., et al., 2011. Multigait soft robot. PNAS, 108(51):20400-20403.
[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).
[78]Simonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409. 1556.
[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.
[80]Sullivan, B.R., Akin, D.L., 2001. A survey of serviceable spacecraft failures. AIAA Space Conf. and Exposition, p.1-8.
[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.
[82]Sutton, R.S., Barto, A.G., 1998. Reinforcement Learning: an Introduction. Volume 1. MIT Press, Cambridge.
[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.
[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.
[85]Theano Development Team, 2016. Theano: a Python framework for fast computation of mathematical expressions. arXiv:1605.02688.
[86]Tisdale, J., Zuwhan, K., Hedrick, J.K., 2009. Autonomous UAV path planning and estimation. IEEE Robot. Autom. Mag., 16(2):35-42.
[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.
[88]Valavanis, K.P., 2007. Introduction. In: Valavanis, K.P. (Ed.), Advances in Unmanned Aerial Vehicles. Springer Netherlands, Dordrecht, p.3-13.
[89]Valavanis, K.P., Vachtsevanos, G.J., 2014. Handbook of Unmanned Aerial Vehicles. Springer Publishing Company.
[90]van Hasselt, H., Guez, A., Silver, D., 2015. Deep reinforcement learning with double q-learning. arXiv:1509.06461.
[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.
[92]Wang, F.Z., 2016. Google released the product of smart home: Google Home. Available from http://tech.163.com/16/ 0519/01/BND3AHMH000915BD.html (in Chinese).
[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.
[94]Wikipedia, 2016a. Unmanned aerial vehicle. Available from https://en.wikipedia.org/wiki/Unmanned_aerial_vehicle
[95]Wikipedia, 2016b. Unmanned combat aerial vehicle. Availabel from https://en.wikipedia.org/wiki/Unmanned_combat_ aerial_vehicle
[96]Wikipedia, 2016c. Human-in-the-loop. Available from https://en.wikipedia.org/wiki/Human-in-the-loop
[97]Xu, K., Ba, J., Kiros, R., et al., 2015. Show, attend and tell: Neural image caption generation with visual attention. arXiv:1502.03044.
[98]Yim, M., Shen, W.M., Salemi, B., et al., 2007. Modular self-reconfigurable robot systems. IEEE Robot. Autom. Mag., 14(1):43-52.
[99]Zhang, D.D., 2016. America upgraded the death UAV to increase the life time and strengthen operational capability. Available from http://www.hdzc.net/html/news/gj/2016_06/16/16175622.html (in Chinese).
[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.
[101]Zimpfer, D., Spehar, P., 1996. STS-71 Shuttle/Mir GNC mission overview. Adv. Astronaut. Sci., 93:441-460.
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