A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 9 Issue 6
Jun.  2022

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 7.847, Top 10% (SCI Q1)
    CiteScore: 13.0, Top 5% (Q1)
    Google Scholar h5-index: 64, TOP 7
Turn off MathJax
Article Contents
H. S. Xia, M. A. Khan, Z. J. Li, and M. C. Zhou, “Wearable robots for human underwater movement ability enhancement: A survey,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 967–977, Jun. 2022. doi: 10.1109/JAS.2022.105620
Citation: H. S. Xia, M. A. Khan, Z. J. Li, and M. C. Zhou, “Wearable robots for human underwater movement ability enhancement: A survey,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 967–977, Jun. 2022. doi: 10.1109/JAS.2022.105620

Wearable Robots for Human Underwater Movement Ability Enhancement: A Survey

doi: 10.1109/JAS.2022.105620
Funds:  This work was supported in part by the National Key Research and Development Program of China (2021YFF0501600), the National Natural Science Foundation of China (U1913601), the Major Science and Technology Projects of Anhui Province (202103a05020004), the China Postdoctoral Science Foundation (2021M693079), the Fundamental Research Funds for the Central Universities (WK2100000020), the State Key Laboratory of Mechanical System and Vibration (MSV202219), the Ministry of Science and Higher Education of the Russian Federation as Part of World-Class Research Center Program: Advanced Digital Technologies (075-15-2020-903), and the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (ICT2022B42)
More Information
  • Underwater robot technology has shown impressive results in applications such as underwater resource detection. For underwater applications that require extremely high flexibility, robots cannot replace skills that require human dexterity yet, and thus humans are often required to directly perform most underwater operations. Wearable robots (exoskeletons) have shown outstanding results in enhancing human movement on land. They are expected to have great potential to enhance human underwater movement. The purpose of this survey is to analyze the state-of-the-art of underwater exoskeletons for human enhancement, and the applications focused on movement assistance while excluding underwater robotic devices that help to keep the temperature and pressure in the range that people can withstand. This work discusses the challenges of existing exoskeletons for human underwater movement assistance, which mainly includes human underwater motion intention perception, underwater exoskeleton modeling and human-cooperative control. Future research should focus on developing novel wearable robotic structures for underwater motion assistance, exploiting advanced sensors and fusion algorithms for human underwater motion intention perception, building up a dynamic model of underwater exoskeletons and exploring human-in-the-loop control for them.

     

  • loading
  • [1]
    Z. Z. Chu, D. Q. Zhu, and S. X. Yang, “Observer-based adaptive neural network trajectory tracking control for remotely operated vehicle,” IEEE Trans. Neural Netw. Learn. Syst., vol. 28, no. 7, pp. 1633–1645, Jul. 2017. doi: 10.1109/TNNLS.2016.2544786
    [2]
    C. Shen, Y. Shi, and B. Buckham, “Trajectory tracking control of an autonomous underwater vehicle using Lyapunov-based model predictive control,” IEEE Trans. Ind. Electron., vol. 65, no. 7, pp. 5796–5805, Jul. 2018. doi: 10.1109/TIE.2017.2779442
    [3]
    T. H. Zhang, J. H. Xiao, L. Li, C. Wang, and G. M. Xie, “Toward coordination control of multiple fish-like robots: Real-time vision-based pose estimation and tracking via deep neural networks,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 12, pp. 1964–1976, Dec. 2021. doi: 10.1109/JAS.2021.1004228
    [4]
    Z. Y. Zhou, J. C. Liu, and J. Z. Yu, “A survey of underwater multi-robot systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 1–18, Jan. 2022. doi: 10.1109/JAS.2021.1004269
    [5]
    Z. X. Wu, J. C. Liu, J. Z. Yu, and H. Fang, “Development of a novel robotic dolphin and its application to water quality monitoring,” IEEE/ASME Trans. Mechatron., vol. 22, no. 5, pp. 2130–2140, Oct. 2017. doi: 10.1109/TMECH.2017.2722009
    [6]
    D. Sward, J. Monk, and N. Barrett, “A systematic review of remotely operated vehicle surveys for visually assessing fish assemblages,” Front. Mar. Sci., vol. 6, p. 134, Mar. 2019.
    [7]
    O. Khatib, X. Yeh, G. Brantner, B. Soe, B. Kim, S. Ganguly, H. Stuart, S. Q. Wang, M. Cutkosky, A. Edsinger, P. Mullins, M. Barham, C. R. Voolstra, K. N. Salama, M. L’hour, and V. Creuze, “Ocean one: A robotic avatar for oceanic discovery,” IEEE Robot. Autom. Mag., vol. 23, no. 4, pp. 20–29, Dec. 2016. doi: 10.1109/MRA.2016.2613281
    [8]
    C. S. Wardle, “Limit of fish swimming speed,” Nature, vol. 255, no. 5511, pp. 725–727, Jun. 1975. doi: 10.1038/255725a0
    [9]
    P. Figueiredo, A. Rouard, J. P. Vilas-Boas, and R. J. Fernandes, “Upper-and lower-limb muscular fatigue during the 200-m front crawl,” Appl. Physiol. Nutr. Metab., vol. 38, no. 7, pp. 716–724, Feb. 2013. doi: 10.1139/apnm-2012-0263
    [10]
    H. Seol, J. C. Suh, and S. Lee, “Development of hybrid method for the prediction of underwater propeller noise,” J. Sound Vib., vol. 288, no. 1–2, pp. 345–360, Nov. 2005. doi: 10.1016/j.jsv.2005.01.015
    [11]
    A. C. Gibb, K. A. Dickson, and G. V Lauder, “Tail kinematics of the chub mackerel Scomber japonicus: Testing the homocercal tail model of fish propulsion,” J. Exp. Biol., vol. 202, no. 18, pp. 2433–2447, Sep. 1999. doi: 10.1242/jeb.202.18.2433
    [12]
    F. E. Fish and C. A. Hui, “Dolphin swimming–a review,” Mamm. Rev., vol. 21, no. 4, pp. 181–195, Dec. 1991. doi: 10.1111/j.1365-2907.1991.tb00292.x
    [13]
    A. von Loebbecke, R. Mittal, F. Fish, and R. Mark, “A comparison of the kinematics of the dolphin kick in humans and cetaceans,” Hum. Mov. Sci., vol. 28, no. 1, pp. 99–112, Feb. 2009. doi: 10.1016/j.humov.2008.07.005
    [14]
    S. Hochstein and R. Blickhan, “Vortex re-capturing and kinematics in human underwater undulatory swimming,” Hum. Mov. Sci., vol. 30, no. 5, pp. 998–1007, Oct. 2011. doi: 10.1016/j.humov.2010.07.002
    [15]
    Z. J. Li, Z. Ren, K. K. Zhao, C. J. Deng, and Y. Feng, “Human-cooperative control design of a walking exoskeleton for body weight support,” IEEE Trans. Ind. Inf., vol. 16, no. 5, pp. 2985–2996, May 2020. doi: 10.1109/TII.2019.2900121
    [16]
    J. Kim, G. Lee, R. Heimgartner, D. A. Revi, N. Karavas, D. Nathanson, I. Galiana, A. Eckert-Erdheim, P. Murphy, D. Perry, N. Menard, D. K. Choe, P. Malcolm, and C. J. Walsh, “Reducing the metabolic rate of walking and running with a versatile, portable exosuit,” Science, vol. 365, no. 6454, pp. 668–672, Aug. 2019. doi: 10.1126/science.aav7536
    [17]
    L. N. Awad, J. Bae, K. O’donnell, S. M. M. De Rossi, K. Hendron, L. H. Sloot, P. Kudzia, S. Allen, K. G. Holt, T. D. Ellis, and C. J. Walsh, “A soft robotic exosuit improves walking in patients after stroke,” Sci. Transl. Med., vol. 9, no. 400, p. eaai9084, Jul. 2017.
    [18]
    P. D. Neuhaus, M. O. O’Sullivan, D. Eaton, J. Carff, and J. E. Pratt, “Concept designs for underwater swimming exoskeletons,” in Proc. IEEE Int. Conf. Robotics and Autom., New Orleans, USA, 2004, pp. 4893−4898.
    [19]
    D. Zeng, “Control of hydraulic drive system of underwater exoskeleton robot,” M.S. thesis, Univ., Elect. Sci. Technol. China, Chengdu, China, 2016.
    [20]
    Q. N. Wang, Z. H. Zhou, Z. D. Zhang, Y. Lou, Y. L. Zhou, S. C. Zhang, W. W. Chen, C. Q. Mao, Z. L. Wang, W. J. Lou, and J. Mai, “An underwater lower-extremity soft exoskeleton for breaststroke assistance,” IEEE Trans. Med. Robot. Bionics, vol. 2, no. 3, pp. 447–462, Aug. 2020. doi: 10.1109/TMRB.2020.2993360
    [21]
    J. J. Zhang, P. Fiers, K. A. Witte, R. W. Jackson, K. L. Poggensee, C. G. Atkeson, and S. H. Collins, “Human-in-the-loop optimization of exoskeleton assistance during walking,” Science, vol. 356, no. 6344, pp. 1280–1284, Jun. 2017. doi: 10.1126/science.aal5054
    [22]
    Y. Ding, M. Kim, S. Kuindersma, and C. J. Walsh, “Human-in-the-loop optimization of hip assistance with a soft exosuit during walking,” Sci. Robot., vol. 3, no. 15, p. eaar5438, Feb. 2018.
    [23]
    G. H. Lin, H. Y. Li, H. Ma, D. Y. Yao, and R. Q. Lu, “Human-in-the-loop consensus control for nonlinear multi-agent systems with actuator faults,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 111–122, Jan. 2022. doi: 10.1109/JAS.2020.1003596
    [24]
    H. Takagi, S. Sugimoto, N. Nishijima, and B. Wilson, “Swimming: Differences in stroke phases, arm-leg coordination and velocity fluctuation due to event, gender and performance level in breaststroke,” Sports Biomech., vol. 3, no. 1, pp. 15–27, Feb. 2004. doi: 10.1080/14763140408522827
    [25]
    M. Rejman, P. Siemontowski, and A. Siemienski, “Comparison of performance of various leg-kicking techniques in fin swimming in terms of achieving the different goals of underwater activities,” PLoS One, vol. 15, no. 8, p. e0236504, Aug. 2020.
    [26]
    A. von Loebbecke, R. Mittal, F. Fish, and R. Mark, “Propulsive efficiency of the underwater dolphin kick in humans,” J. Biomech. Eng., vol. 131, no. 5, p. 054504, May 2009.
    [27]
    M. Strzała, P. Krężałek, M. Kaca, G. Głąb, A. Ostrowski, A. Stanula, and A. Tyka, “Swimming speed of the breaststroke kick,” J. Hum. Kinet., vol. 35, no. 1, pp. 133–139, Dec. 2012. doi: 10.2478/v10078-012-0087-4
    [28]
    K. Seo, S. J. Chung, and J. J. E. Slotine, “CPG-based control of a turtle-like underwater vehicle,” Auton. Robots, vol. 28, no. 3, pp. 247–269, Jan. 2010. doi: 10.1007/s10514-009-9169-0
    [29]
    P. J. Zhou, T. Liu, X. H. Zhou, J. G. Mou, S. H. Zheng, Y. Q. Gu, and D. H. Wu, “Overview of progress in development of the bionic underwater propulsion system,” J. Biomimetics,Biomater. Biomed. Eng., vol. 32, pp. 9–19, May 2017. doi: 10.4028/www.scientific.net/JBBBE.32.9
    [30]
    S. Samimy, J. C. Mollendorf, and D. R. Pendergast, “A theoretical and experimental analysis of diver technique in underwater fin swimming,” Sport. Eng., vol. 8, no. 1, pp. 27–38, Mar. 2005. doi: 10.1007/BF02844129
    [31]
    D. R. Pendergast, J. Mollendorf, C. Logue, and S. Samimy, “Evaluation of fins used in underwater swimming,” Undersea Hyperb. Med., vol. 30, no. 1, pp. 57–73, Feb. 2003.
    [32]
    J. K. Gao, “Research on the control method of underwater booster robot,” M.S. thesis, Univ., Elect. Sci. Technol. China, Chengdu, China, 2018.
    [33]
    R. Qin, “Study on dynamic characteristics of underwater exoskeleton robots,” M.S. thesis, Univ., Elect. Sci. Technol. China, Chengdu, China, 2017.
    [34]
    B. Wang, “Research on human motion intention perception method of underwater booster robot,” M.S. thesis, Univ., Elect. Sci. Technol. China, Chengdu, China, 2020.
    [35]
    N. Jarrassé and G. Morel, “Connecting a human limb to an exoskeleton,” IEEE Trans. Robot., vol. 28, no. 3, pp. 697–709, Jun. 2012. doi: 10.1109/TRO.2011.2178151
    [36]
    P. B. Shull and H. S. Xia, “Modeling and prediction of wearable energy harvesting sliding shoes for metabolic cost and energy rate outside of the lab,” Sensors, vol. 20, no. 23, p. 6915, Dec. 2020.
    [37]
    Z. J. Li, Y. X. Yuan, L. Luo, W. B. Su, K. K. Zhao, C. C. Xu, J. L. Huang, and M. Pi, “Hybrid brain/muscle signals powered wearable walking exoskeleton enhancing motor ability in climbing stairs activity,” IEEE Trans. Med. Robot. Bionics, vol. 1, no. 4, pp. 218–227, Nov. 2019. doi: 10.1109/TMRB.2019.2949865
    [38]
    B. Vanderborght, A. Albu-Schaeffer, A. Bicchi, E. Burdet, D. G. Caldwell, R. Carloni, M. Catalano, O. Eiberger, W. Friedl, G. Ganesh, M. Garabini, M. Grebenstein, G. Grioli, S. Haddadin, H. Hoppner, A. Jafari, M. Laffranchi, D. Lefeber, F. Petit, S. Stramigioli, N. Tsagarakis, M. Van Damme, R. Van Ham, L. C. Visser, and S. Wolf, “Variable impedance actuators: A review,” Rob. Auton. Syst., vol. 61, no. 12, pp. 1601–1614, Dec. 2013. doi: 10.1016/j.robot.2013.06.009
    [39]
    A. B. Zoss, H. Kazerooni, and A. Chu, “Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX),” IEEE/ASME Trans. Mechatron., vol. 11, no. 2, pp. 128–138, Apr. 2006. doi: 10.1109/TMECH.2006.871087
    [40]
    W. van Dijk, H. van der Kooij, and E. Hekman, “A passive exoskeleton with artificial tendons: Design and experimental evaluation,” in Proc. IEEE Int. Conf. Rehabilitation Robotics, Zurich, Switzerland, 2011, pp. 1−6.
    [41]
    Y. L. Park, B. R. Chen, N. O. Pérez-Arancibia, D. Young, L. Stirling, R. J. Wood, E. C. Goldfield, and R. Nagpal, “Design and control of a bio-inspired soft wearable robotic device for ankle–foot rehabilitation,” Bioinspir. Biomim., vol. 9, no. 1, p. 016007, Jan. 2014.
    [42]
    Y. Cao and J. Huang, “Neural-network-based nonlinear model predictive tracking control of a pneumatic muscle actuator-driven exoskeleton,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1478–1488, Nov. 2020. doi: 10.1109/JAS.2020.1003351
    [43]
    J. Kwon, S. J. Yoon, and Y. L. Park, “Flat inflatable artificial muscles with large stroke and adjustable force–length relations,” IEEE Trans. Robot., vol. 36, no. 3, pp. 743–756, Jun. 2020. doi: 10.1109/TRO.2019.2961300
    [44]
    S. J. Park and C. H. Park, “Suit-type wearable robot powered by shape-memory-alloy-based fabric muscle,” Sci. Rep., vol. 9, no. 1, p. 6024, Jun. 2019.
    [45]
    F. L. Haufe, A. M. Kober, K. Schmidt, A. Sancho-Puchades, J. E. Duarte, P. Wolf, and R. Riener, “User-driven walking assistance: First experimental results using the MyoSuit,” in Proc. IEEE 16th Int. Conf. Rehabilitation Robotics (ICORR), Toronto, Canada, 2019, pp. 944−949.
    [46]
    H. S. Xia, J. Kwon, P. Pathak, J. Ahn, P. B. Shull, and Y. L. Park, “Design of a multi-functional soft ankle exoskeleton for foot-drop prevention, propulsion assistance, and inversion/eversion stabilization,” in Proc. 8th IEEE RAS/EMBS Int. Conf. Biomedical Robotics and Biomechatronics (BioRob), New York, USA, 2020, pp. 118−123.
    [47]
    P. B. Shull, H. S. Xia, J. M. Charlton, and M. A. Hunt, “Wearable real-time haptic biofeedback foot progression angle gait modification to assess short-term retention and cognitive demand,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 29, pp. 1858–1865, Sep. 2021. doi: 10.1109/TNSRE.2021.3110202
    [48]
    D. Chiaradia, M. Xiloyannis, C. W. Antuvan, A. Frisoli, and L. Masia, “Design and embedded control of a soft elbow exosuit,” in Proc. IEEE Int. Conf. Soft Robotics (RoboSoft), Livorno, Italy, 2018, pp. 565−571.
    [49]
    M. L. Zhu, Z. D. Sun, T. Chen, and C. Lee, “Low cost exoskeleton manipulator using bidirectional triboelectric sensors enhanced multiple degree of freedom sensory system,” Nat. Commun., vol. 12, no. 1, p. 2692, May 2021.
    [50]
    F. Dadashi, A. Arami, F. Crettenand, G. P. Millet, J. Komar, L. Seifert, and K. Aminian, “A hidden Markov model of the breaststroke swimming temporal phases using wearable inertial measurement units,” in Proc. IEEE Int. Conf. body Sensor Networks, Cambridge, USA, 2013, pp. 1−6.
    [51]
    Z. D. Zhang, D. F. Xu, Z. H. Zhou, J. G. Mai, Z. K. He, and Q. N. Wang, “IMU-based underwater sensing system for swimming stroke classification and motion analysis,” in Proc. IEEE Int. Conf. Cyborg and Bionic Systems (CBS), Beijing, China, 2017, pp. 268−272.
    [52]
    G. R. Naik, A. H. Al-Timemy, and H. T. Nguyen, “Transradial amputee gesture classification using an optimal number of sEMG sensors: An approach using ICA clustering,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 24, no. 8, pp. 837–846, Aug. 2016. doi: 10.1109/TNSRE.2015.2478138
    [53]
    K. Gui, H. H. Liu, and D. G. Zhang, “A practical and adaptive method to achieve EMG-based torque estimation for a robotic exoskeleton,” IEEE/ASME Trans. Mechatron., vol. 24, no. 2, pp. 483–494, Apr. 2019. doi: 10.1109/TMECH.2019.2893055
    [54]
    C. G. Yang, C. Zeng, C. Fang, W. He, and Z. J. Li, “A DMPs-based framework for robot learning and generalization of humanlike variable impedance skills,” IEEE/ASME Trans. Mechatron., vol. 23, no. 3, pp. 1193–1203, Jun. 2018. doi: 10.1109/TMECH.2018.2817589
    [55]
    J. H. Park and K. D. Kim, “Biped robot walking using gravity-compensated inverted pendulum mode and computed torque control,” in Proc. IEEE Int. Conf. Robotics and Automation, Leuven, Belgium, 1998, pp. 3528−3533.
    [56]
    D. J. Villarreal and R. D. Gregg, “Unified phase variables of relative degree two for human locomotion,” in Proc. 38th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, USA, 2016, pp. 6262−6267.
    [57]
    R. D. Gregg, E. J. Rouse, L. J. Hargrove, and J. W. Sensinger, “Evidence for a time-invariant phase variable in human ankle control,” PLoS One, vol. 9, no. 2, p. e89163, Feb. 2014.
    [58]
    Z. J. Li, Z. C. Huang, W. He, and C. Y. Su, “Adaptive impedance control for an upper limb robotic exoskeleton using biological signals,” IEEE Trans. Ind. Electron., vol. 64, no. 2, pp. 1664–1674, Feb. 2017. doi: 10.1109/TIE.2016.2538741
    [59]
    Z. J. Li, B. Huang, Z. F. Ye, M. D. Deng, and C. G. Yang, “Physical human-robot interaction of a robotic exoskeleton by admittance control,” IEEE Trans. Ind. Electron., vol. 65, no. 12, pp. 9614–9624, Dec. 2018. doi: 10.1109/TIE.2018.2821649
    [60]
    Y. T. He, D. Eguren, J. M. Azorín, R. G. Grossman, T. P. Luu, and J. L. Contreras-Vidal, “Brain–machine interfaces for controlling lower-limb powered robotic systems,” J. Neural Eng., vol. 15, no. 2, p. 021004, Feb. 2018.
    [61]
    D. X. Liu, J. Xu, C. J. Chen, X. G. Long, D. C. Tao, and X. Y. Wu, “Vision-assisted autonomous lower-limb exoskeleton robot,” IEEE Trans. Syst.,Man,Cybern.: Syst., vol. 51, no. 6, pp. 3759–3770, Jun. 2021. doi: 10.1109/TSMC.2019.2932892
    [62]
    D. F. Xu, X. H. Liu, and Q. N. Wang, “Knee exoskeleton assistive torque control based on real-time gait event detection,” IEEE Trans. Med. Robot. Bionics, vol. 1, no. 3, pp. 158–168, Aug. 2019. doi: 10.1109/TMRB.2019.2930352
    [63]
    S. Oh and K. Kong, “High-precision robust force control of a series elastic actuator,” IEEE/ASME Trans. Mechatron., vol. 22, no. 1, pp. 71–80, Feb. 2017. doi: 10.1109/TMECH.2016.2614503
    [64]
    H. Shimojo, R. Nara, Y. Baba, H. Ichikawa, Y. Ikeda, and Y. Shimoyama, “Does ankle joint flexibility affect underwater kicking efficiency and three-dimensional kinematics?” J. Sports Sci., vol. 37, no. 20, pp. 2339–2346, Jun. 2019. doi: 10.1080/02640414.2019.1633157
    [65]
    J. B. Chossat, D. K. Y. Chen, Y. L. Park, and P. B. Shull, “Soft wearable skin-stretch device for haptic feedback using twisted and coiled polymer actuators,” IEEE Trans. Haptics, vol. 12, no. 4, pp. 521–532, Oct.–Dec. 2019. doi: 10.1109/TOH.2019.2943154
    [66]
    J. M. Donelan, Q. Li, V. Naing, J. A. Hoffer, D. J. Weber, and A. D. Kuo, “Biomechanical energy harvesting: Generating electricity during walking with minimal user effort,” Science, vol. 319, no. 5864, pp. 807–810, Feb. 2008. doi: 10.1126/science.1149860
    [67]
    S. H. Collins, M. B. Wiggin, and G. S. Sawicki, “Reducing the energy cost of human walking using an unpowered exoskeleton,” Nature, vol. 522, no. 7555, pp. 212–215, Apr. 2015. doi: 10.1038/nature14288
    [68]
    S. S. Yun, K. Kim, J. Ahn, and K. J. Cho, “Body-powered variable impedance: An approach to augmenting humans with a passive device by reshaping lifting posture,” Sci. Robot., vol. 6, no. 57, p. eabe1243, Aug. 2021.
    [69]
    N. Siddiqui and R. H. M. Chan, “Hand gesture recognition using multiple acoustic measurements at wrist,” IEEE Trans. Hum.-Mach. Syst., vol. 51, no. 1, pp. 56–62, Feb. 2020.
    [70]
    S. Wilson and R. Vaidyanathan, “Upper-limb prosthetic control using wearable multichannel mechanomyography,” in Proc. Int. Conf. Rehabilitation Robotics (ICORR), London, UK, 2017, pp. 1293−1298.
    [71]
    H. F. Wu, Q. Huang, D. Q. Wang, and L. F. Gao, “A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals,” J. Electromyogr. Kinesiol., vol. 42, pp. 136–142, Oct. 2018. doi: 10.1016/j.jelekin.2018.07.005
    [72]
    N. Strokina, J. K. Kämäräinen, J. A. Tuhtan, J. F. Fuentes-Pérez, and M. Kruusmaa, “Joint estimation of bulk flow velocity and angle using a lateral line probe,” IEEE Trans. Instrum. Meas., vol. 65, no. 3, pp. 601–613, Mar. 2016. doi: 10.1109/TIM.2015.2499019
    [73]
    R. Venturelli, O. Akanyeti, F. Visentin, J. Ježov, L. D. Chambers, G. Toming, J. Brown, M. Kruusmaa, W. M. Megill, and P. Fiorini, “Hydrodynamic pressure sensing with an artificial lateral line in steady and unsteady flows,” Bioinspir. Biomim., vol. 7, no. 3, p. 036004, Apr. 2012.
    [74]
    X. Liu, Q. Zhang, and G. H. Gao, “Solvent-resistant and nonswellable hydrogel conductor toward mechanical perception in diverse liquid media,” ACS Nano, vol. 14, no. 10, pp. 13709–13717, Sep. 2020. doi: 10.1021/acsnano.0c05932
    [75]
    A. Von Loebbecke, R. Mittal, R. Mark, and J. Hahn, “A computational method for analysis of underwater dolphin kick hydrodynamics in human swimming,” Sports Biomech., vol. 8, no. 1, pp. 60–77, Feb. 2009. doi: 10.1080/14763140802629982
    [76]
    Y. Ishii, S. Nishikawa, R. Niiyama, and Y. Kuniyoshi, “Development of a musculoskeletal humanoid robot as a platform for biomechanical research on the underwater dolphin kick,” in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, Madrid, Spain, 2018, pp. 3285−3291.
    [77]
    W. T. Wu, C. J. Yang, Z. Xu, X. Wu, Y. C. Zhu, and Q. X. Wei, “Development and control of a humanoid underwater robot,” in Proc. 6th Int. Conf. Mechatronics and Robotics Engineering, Barcelona, Spain, 2020, pp. 6−11.
    [78]
    D. Wei, Z. J. Li, Q. Wei, H. Su, B. Song, W. He, and J. Q. Li, “Human-in-the-loop control strategy of unilateral exoskeleton robots for gait rehabilitation,” IEEE Trans. Cogn. Dev. Syst., vol. 13, no. 1, pp. 57–66, Mar. 2021. doi: 10.1109/TCDS.2019.2954289
    [79]
    H. Ahmadi, M. Gholamzadeh, L. Shahmoradi, M. Nilashi, and P. Rashvand, “Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review,” Comput. Methods Programs Biomed., vol. 161, pp. 145–172, Jul. 2018. doi: 10.1016/j.cmpb.2018.04.013
    [80]
    M. Karakose, “An improved nonstationary fuzzy system approach versus type-2 fuzzy system for the lifting motion control with human-in-the-loop simulation,” Int. J. Comput. Intell. Syst., vol. 11, no. 1, pp. 183–194, Jan. 2018. doi: 10.2991/ijcis.11.1.14
    [81]
    J. Gupta, R. Datta, A. K. Sharma, A. Segev, and B. Bhattacharya, “Evolutionary computation for optimal LQR weighting matrices for lower limb exoskeleton feedback control,” in Proc. IEEE Int. Conf. Computational Science and Engineering and IEEE Int. Conf. Embedded and Ubiquitous Computing, New York, USA, 2019, pp. 24−29.
    [82]
    T. Zhang and M. Nakamura, “Neural network-based hybrid human-in-the-loop control for meal assistance orthosis,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 14, no. 1, pp. 64–75, Mar. 2006. doi: 10.1109/TNSRE.2005.863840
    [83]
    M. Ghahramani, Y. Qiao, M. C. Zhou, A. O’Hagan, and J. Sweeney, “AI-based modeling and data-driven evaluation for smart manufacturing processes,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1026–1037, Jul. 2020. doi: 10.1109/JAS.2020.1003114
    [84]
    Y. F. Ma, Z. Y. Wang, H. Yang, and L. Yang, “Artificial intelligence applications in the development of autonomous vehicles: A survey,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 315–329, Mar. 2020. doi: 10.1109/JAS.2020.1003021
    [85]
    X. Luo, J. P. Sun, Z.D. Wang, S. Li, and M. S. Shang, “Symmetric and non-negative latent factor models for undirected, high dimensional and sparse networks in industrial applications,” IEEE Trans. Industrial Informatics, vol. 13, no. 6, pp. 3098−3107, Jun. 2017.
    [86]
    Z. C. Cao, C. R. Lin, M. C. Zhou, and R. Huang, “Scheduling semiconductor testing facility by using cuckoo search algorithm with reinforcement learning and surrogate modeling,” IEEE Trans. Autom. Science and Engineering, vol.16, no. 2, pp. 825−837, Apr. 2019.
    [87]
    M. J. Cui, L. Li, M. C. Zhou, and A. Abusorrah, “Surrogate-assisted autoencoder-embedded evolutionary optimization algorithm to solve high-dimensional expensive Problems,” IEEE Trans. Evolutionary Computation, DOI: 10.1109/TEVC.2021.3113923, 2021.

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(5)  / Tables(2)

    Article Metrics

    Article views (229) PDF downloads(54) Cited by()

    Highlights

    • This survey analyzes the state-of-the-art of underwater exoskeleton for human enhancement
    • Challenges: underwater motion intention perception, underwater exoskeleton modeling and control
    • Future direction: novel structures, sensors & fusion, underwater dynamic models

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return