Open Access
Volume 40, Number 3, June 2022
Page(s) 576 - 583
Published online 19 September 2022
  1. ZHU Y, TANG X. Overview of swarm intelligence[C]//2010 International Conference on Computer Application and System Modeling, 2010: 400-403 [Google Scholar]
  2. TAMÁS Vicsek. Collective motion[J]. Physics Reports, 2012, 517(3/4): 71–140 [Article] [CrossRef] [Google Scholar]
  3. WEIHS D. Hydrodynamics of fish schooling[J]. Nature, 1973, 241: 290–291 [Article] [CrossRef] [Google Scholar]
  4. HEMELRIJK C, REID D, HILDENBRANDT H, et al. The increased efficiency of fish swimming in a school[J]. Fish and Fisheries, 2015, 16(3): 511–521 [Article] [CrossRef] [Google Scholar]
  5. FIELDS P A. Decreased swimming effort in groups of Pacific mackerel(scomber japonicus)[J]. American Society of Zoology, 1990, 30(4): 134A [Google Scholar]
  6. HERSKIN J, STEFFENSEN J F. Energy savings in sea bass swimming in a school: measurements of tail beat frequency and oxygen consumption at different swimming speeds[J]. Journal of Fish Biology, 2010, 53(2): 366–376 [Article] [CrossRef] [Google Scholar]
  7. KILLEN S S, MARRAS S, STEFFENSEN J F, et al. Aerobic capacity influences the spatial position of individuals within fish schools[J]. Proceedings Biological of the Royal Society, 2012, 279(1727): 357–364 [Article] [CrossRef] [Google Scholar]
  8. HUANG Q, ZHANG D, PAN G. Computational model construction and analysis of the hydrodynamics of a rhinopter jovanica[J]. IEEE Aecess, 2020, 8: 30410–30420 [Article] [CrossRef] [Google Scholar]
  9. HE J, CAO Y, HUANG Q, et al. A new type of bionic manta ray robot[C]//Global Oceans 2020, Singapore, 2020: 1–6 [Google Scholar]
  10. VERMA S, NOVATI G, KOUMOUTSAKOS P. Efficient collective swimming by harnessing vortices through deep reinforcement learning[J]. Proceedings of the National Academy of Sciences, 2018, 115(23): 5849–5854 [Article] [CrossRef] [Google Scholar]
  11. CHEN S Y, CHEN Y C, CHI K J, et al. The swimming patterns and energy-saving mechanism revealed from three fish in a school[J]. Ocean Engineering, 2016, 122(1): 22–31 [Article] [CrossRef] [Google Scholar]
  12. GAO Pengcheng, HUANG Qiaogao, PAN Guang, et al. Experimental study on hydrodynamic characteristics of flexible flapping wing[J]. Journal of Huazhong University of Science and Technology, 2022, 50(1): 144–148 [Article] (in Chinese) [Google Scholar]
  13. XING Cheng, PAN Guang, HUANG Qiaogao. Performance analysis of airfoil flow field of manta ray like flexible submersible[J]. Digital Ocean and Underwater Attack and Defense, 2020, 3(3): 265–270 [Article] (in Chinese) [Google Scholar]
  14. GUIJIE L, ANYI W, XINBAO W, et al. A review of artificial lateral line in sensor fabrication and bionic applications for robot fish[J]. Applied Bionics and Biomechanics, 2016, 2016: 1–15 [Article] [Google Scholar]
  15. HU Qiao, LIU Yu, ZHAO Zhenyi, et al. Research progress of underwater unmanned cluster bionic artificial side line detection technology[J]. Journal of Underwater Unmanned Systems, 2019, 27(2): 114–122 [Article] (in Chinese) [Google Scholar]
  16. LIU G, WANG M, WANG A, et al. Research on flow field perception based on artificial lateral line sensor system[J]. Sensors, 2018, 18(3): 838 [Article] [CrossRef] [Google Scholar]
  17. ASADNIA M, KOTTAPALLI A G P, MIAO J, et al. Artificial fish skin of self-powered micro-electromechanical systems hair cells for sensing hydrodynamic flow phenomena[J]. Journal of the Royal Society Interface, 2015, 12(111): 322 [Article] [Google Scholar]
  18. ZHANG Xingxing, WANG Wei, CHEN Shiming, et al. Research on perception of adjacent bionic robotic fish based on artificial side line[J]. Measurement and Control Technology, 2016, 35(10): 33–37 [Article] (in Chinese) [Google Scholar]
  19. ZHENG XWANG CFAN R, et al. Artificial lateral line based local sensing between two adjacent robotic fish[J]. Bioinspiration & Biomimetics, 2017, 13(1): 016002 [Article] [CrossRef] [Google Scholar]
  20. ZHANG F, LAGOR F D, YEO D, et al. Distributed flow sensing for closed-loop speed control of a flexible fish robot[J]. Bioinspiration & Biomimetics, 2015, 10(6): 065001 [Article] [CrossRef] [Google Scholar]
  21. FREE B A, PALEY D. Model-based observer and feedback control design for a rigid Joukowski foil in a Kármán vortex street[J]. Bioinspiration & Biomimetics, 2018, 13(3): 035001 [Article] [CrossRef] [Google Scholar]
  22. IJSPEERT A J, CRESPI A, RYCZKO D, et al. From swimming to walking with a salamander robot driven by a spinal cord model[J]. Science, 2007, 315(5817): 1416–1420 [Article] [CrossRef] [Google Scholar]
  23. CAO Y, LU Y, CAI Y, et al. CGP-fuzzy-based control of a cownose-ray-like fish robot[J]. Industrial Robot, 2019, 461(6): 779–791 [Article] [CrossRef] [Google Scholar]
  24. LA SPINA G, SFAKIOTAKIS M, TSAKIRIS D P, et al. Polychaete-like undulatory robotic locomotion in unstructured substrates[J]. IEEE Trans on Robotics, 2007, 23(6): 1200–1212 [Article] [CrossRef] [Google Scholar]
  25. LOW K H. Mechatronics and buoyancy implementation of robotic fish swimming with modular fin mechanisms[J]. Journal of Systems and Control Engineering, 2007, 221(3): 295–309 [Article] [Google Scholar]
  26. ROUT R, SUBUDHI B. A backstepping approach for the formation control of multiple autonomous underwater vehicles using a leader-follower strategy[J]. Journal of Marine Engineering & Technology, 2016, 15(1): 38–46 [CrossRef] [Google Scholar]
  27. DESAI J P, KUMAR V. Modeling and control of formations of nonholonomic mobile robots[J]. IEEE Trans on Robotics & Automation, 2002, 17(6): 905–908 [Article] [Google Scholar]
  28. LEE G, CHWA D. Decentralized behavior-based formation control of multiple robots considering obstacle avoidance[J]. Intelligent Service Robotics, 2017, 11: 127–138 [Article] [Google Scholar]
  29. YOSHIOKA C, NAMERIKAWA T. Formation control of nonholonomic multi-vehicle systems based on virtual structure[J]. IFAC Proceedings Volumes, 2008, 41(2): 5149–5154 [Article] [CrossRef] [Google Scholar]
  30. JIA Y, WANG L. Leader-follower flocking of multiple robotic fish[J]. IEEE Trans on Mechatronics, 2015, 20(3): 1372–1383 [Article] [CrossRef] [Google Scholar]
  31. CUI R, GE S S. Leader-follower formation control of underactuated autonomous underwater vehicles[J]. Ocean Engineering, 2010, 37(17/18): 1491m1502 [Article] [CrossRef] [Google Scholar]
  32. YANG Panpan, LIU Mingyong, LEI Xiaokang, et al. Research progress on clustering behavior modeling and control of clustered systems[J]. Control and Decision Making, 2016(2): 193–206 [Article] (in Chinese) [Google Scholar]
  33. LIU Mingyong, LEI Xiaokang, YANG Panpan, et al. Self organizing clustering method of cluster system based on information coupling[J]. Control and Decision Making, 2015(2): 271–276 [Article] (in Chinese) [Google Scholar]
  34. DING G, ZHU D, SUN B. Formation control and obstacle avoidance of multi-AUV for 3-D underwater environment[C]//Proceedings of the 33rd Chinese Control Conference, 2014: 8347-8352 [Google Scholar]
  35. JIA Q, LI G. Formation control and obstacle avoidance algorithm of multiple autonomous underwater vehicles(AUVs) based on potential function and behavior rules[C]//2007 IEEE International Conference on Automation and Logistics, 2007: 569–573 [Google Scholar]
  36. PANG S, LI Y, YI H. Joint formation control with obstacle avoidance of towfish and multiple autonomous underwater vehicles based on graph theory and the null-space-based method[J]. Sensors, 2019, 19(11): 2591 [Article] [CrossRef] [Google Scholar]
  37. LIANG X, MU X, HOU Y, et al. Energy efficiency formation optimization of a fleet of AUVs based on multi-island genetic algorithm[C]//36th Chinese Control Conference, 2017: 6681–6684 [Google Scholar]
  38. LI L, NAGY M, GRAVING J M, et al. Vortex phase matching as a strategy for schooling in robots and in fish[J]. Nature Communications, 2020, 11(1): 1–9 [Article] [CrossRef] [PubMed] [Google Scholar]
  39. FU Rubin, LI Liang, XU Cheng, et al. Research on energy saving of bionic robotic fish based on reinforcement learning[J]. Journal of Peking University, 2019, 55(3): 12–17 [Article] (in Chinese) [Google Scholar]
  40. SANTOS C DCILDOZ M UVIEIRA R P, et al. Nonlinear mapping for performance improvement and energy saving of underwater vehicles[J]. International Journal of Robust and Nonlinear Control, 2018, 28(18): 5811–5840 [Article] [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.