Advertisement

Microsystem Technologies

, Volume 25, Issue 2, pp 499–508 | Cite as

Underwater motion characteristics evaluation of multi amphibious spherical robots

  • Yanlin He
  • Lianqing ZhuEmail author
  • Guangkai Sun
  • Junfei Qiao
  • Shuxiang Guo
Technical Paper
  • 62 Downloads

Abstract

Information exchanges and cooperative movements of multi robots have become a hot topic in robotics. To improve the performance and working efficiency of our amphibious spherical robot, the leader–follower method, which could realize the coordinated movement and formation keeping for three or more robots, was adopted in this paper. Firstly, this paper depicts the formation design of multi robots, and the formation system is made up of two or three robots which can formed longitudinal formation, linear formation and triangular formation. And then, the formation strategy of multi robots based on leader–follower method was depicted and analyzed, including the principle of relative attitude observation and the design of kinematic controller. Finally, based on the theoretical analysis and calculation, a series of underwater experiments were carried out to test the performance of amphibious spherical multi robots with different formations; these experiments included longitudinal formation motion test, linear formation motion test, and triangular formation motion test. The experimental results demonstrated that the multi robots could realize different underwater formation motion accurately.

Notes

Acknowledgements

This work is supported by Program for Changjiang Scholars and Innovative Research Team in University (no. IRT_16R07). This research project was also partly supported by the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (no. IDHT20170510), China Postdoctoral Science Foundation (2018M631290).

References

  1. Anirban M, Asada HH (2014) Control-configured design of spheroidal, appendage-free, underwater vehicle. IEEE Trans Robot 30(2):448–460CrossRefGoogle Scholar
  2. Capitan J, Spaan MT, Merino L, Ollero A (2013) Decentralized multi-robot cooperation with auctioned POMDPs. Int J Robot Res 32(6):650–671CrossRefGoogle Scholar
  3. Eren F, Pe’eri S, Thein MW et al (2017) Position, orientation and velocity detection of unmanned underwater vehicles (UUVs) using an optical detector array. Sensors 17(8):1741CrossRefGoogle Scholar
  4. Fang X, Yan W, Zhang F, Li J (2014) Formation geometry of underwater positioning based on multiple USV/AUV. Syst Eng Electron 36(5):947–951.  https://doi.org/10.3969/j.issn.1001-506x.2014.05.22 Google Scholar
  5. Gao W, Liu Y, Xu B, Che Y (2014) An improved cooperative localization method for multiple autonomous underwater vehicles based on acoustic round-trip ranging. In: Position, location and navigation symposium - PLANS, Monterey, CA, USA, pp 1420–1423.  https://doi.org/10.1109/PLANS.2014.6851518
  6. Guo S, Shi L, Mao S, Li M (2012) Design and kinematic analysis of an amphibious spherical robot. In: Proceedings of IEEE international conference on mechatronics and automation, Chengdu, China, pp 2214–2219.  https://doi.org/10.1109/ICMA.2012.6285687
  7. Guo S, He Y, Shi L, Pan S, Tang K, Xiao R, Guo P (2016) Modal and fatigue analysis of critical components of an amphibious spherical robot. Microsyst Technol 23(6):1–15Google Scholar
  8. Guo S, Pan S, Shi L, Guo P, He Y, Tang K (2017) Visual detection and tracking system for a spherical amphibious robot. Sensors 17(4):870CrossRefGoogle Scholar
  9. Guo S, He Y, Shi L, Pan S, Xiao R, Tang K, Guo P (2018) Modeling and experimental evaluation of an improved amphibious robot with compact structure. Robot Comput Integr Manuf 51:37–52CrossRefGoogle Scholar
  10. He Y, Shi L, Guo S, Pan S, Wang Z (2014) 3D printing technology-based an amphibious spherical underwater robot. In: Proceedings of IEEE international conference on mechatronics and automation, Tianjin, China, pp 1382–1387.  https://doi.org/10.1109/ICMA.2014.6885901
  11. He Y, Shi L, Guo S, Pan S, Wang Z (2016) Preliminary mechanical analysis of an improved amphibious spherical father robot. Microsyst Technol 22(8):2051–2066CrossRefGoogle Scholar
  12. Jiang J, Zhou L, Zhang J (2014) Multi-robot cooperation behavior decision based on psychological values. Sens Transducers 162(1):299–307Google Scholar
  13. Kelasidi E, Liljeback P, Pettersen KY, Gravdahl JT (2016) Innovation in underwater robots: biologically inspired swimming snake robots. IEEE Robot Autom Mag 23(1):44–62CrossRefGoogle Scholar
  14. Krogstad TR (2010) Attitude synchronization in spacecraft formations theoretical and experimental results. PhD Thesis, NTNUGoogle Scholar
  15. Li X, Guo J, Guo S (2015a) OFDM-based micro-signal communication method for the spherical amphibious underwater vehicle. In: Proceedings of IEEE international conference on mechatronics and automation, Beijing, China, pp 2094–2099.  https://doi.org/10.1109/ICMA.2015.7237809
  16. Li M, Guo S, Hirata H, Ishihara H (2015b) Design and performance evaluation of an amphibious spherical robot. Robot Auton Syst 64:21–34CrossRefGoogle Scholar
  17. Li Y, Guo S, Wang Y (2017) Design and characteristics evaluation of a novel spherical underwater robot. Robot Auton Syst 94:61–74CrossRefGoogle Scholar
  18. Liang X, Liu Y, Wang H, Chen W, Xing K, Liu T (2016) Leader-following formation tracking control of mobile robots without direct position measurements. IEEE Trans Autom Control 61(12):4131–4137MathSciNetCrossRefzbMATHGoogle Scholar
  19. Makavita CD, Nguyen HD, Jayasinghe SG, Ranmuthugala D (2017) Predictor-based model reference adaptive control of an unmanned underwater vehicle. In: International conference on control, automation, robotics and vision, Phuket, Thailand, pp 1–7.  https://doi.org/10.1109/ICARCV.2016.7838851
  20. Oh H, Shirazi AR, Sun C, Jin Y (2017) Bio-inspired self-organising multi-robot pattern formation: a review. Robot Auton Syst 91(C):83–100CrossRefGoogle Scholar
  21. Pan S, Guo S, Shi L, He Y, Wang Z, Huang Q (2014) A spherical robot based on all programmable SoC and 3-D printing. In: Proceedings of IEEE international conference on mechatronics and automation, Tianjin, China, pp 150–155.  https://doi.org/10.1109/ICMA.2014.6885687
  22. Pan S, Shi L, Guo S (2015) A kinect-based real-time compressive tracking prototype system for amphibious spherical robots. Sensors 15(4):8232–8252CrossRefGoogle Scholar
  23. Pan S, Guo S, Shi L, Tang K, He Y (2016) An adaptive compressive tracking algorithm for amphibious spherical robots. In: Proceedings of IEEE international conference on mechatronics and automation, Harbin, China, pp 605–611.  https://doi.org/10.1109/ICMA.2016.7558632
  24. Pellegrinelli S, Pedrocchi N, Tosatti LM, Fischer A, Tolio T (2017) Multi-robot spot-welding cells for car-body assembly: design and motion planning. Robot Comput Integr Manuf 44(C):97–116CrossRefGoogle Scholar
  25. Shi L, He Y, Guo S (2013a) Skating motion analysis of the amphibious quadruped mother robot. In: Proceedings of IEEE international conference on mechatronics and automation, Takamatsu, Japan, pp 1749–1754.  https://doi.org/10.1109/ICMA.2013.6618180
  26. Shi L, Guo S, Mao S, Yue C, Li M, Asaka K (2013b) Development of an amphibious turtle-inspired spherical mother robot. J Bionic Eng 10(4):446–455CrossRefGoogle Scholar
  27. Shi L, Tang K, Guo S, Bao X, Pan S, Guo P (2016) Leader-follower cooperative movement method for multiple amphibious spherical robots. In: Proceedings of IEEE international conference on mechatronics and automation, Harbin, China, pp 593–598.  https://doi.org/10.1109/ICMA.2016.7558630
  28. Shintake J, Shea H, Floreano D (2016) Biomimetic underwater robots based on dielectric elastomer actuators. In: International conference on intelligent robots and systems, Daejeon, South Korea, pp 4957–4962.  https://doi.org/10.1109/IROS.2016.7759728
  29. Shojaei K (2016) Neural network formation control of underactuated autonomous underwater vehicles with saturating actuators. Neurocomputing 194(5):372–384MathSciNetCrossRefGoogle Scholar
  30. Sutantyo D, Levi P, Moslinger C, Read M (2013) Collective-adaptive Lévy flight for underwater multi-robot exploration. In: Proceedings of IEEE international conference on mechatronics and automation, Takamatsu, Japan, pp 456–462.  https://doi.org/10.1109/ICMA.2013.6617961
  31. Tsiogkas N, Saigol Z, Lane DM (2015) Distributed multi-AUV cooperation methods for underwater archaeology. In: MTS/IEEE OCEANS, Genova, Italy, pp 1–5.  https://doi.org/10.1109/OCEANS-Genova..7271549
  32. Wang S, Kang F, Hong H (2017a) Research on control and decision-making of submarine and intelligent UUV cooperative system. Acta Armamentarii 38(2):335–344Google Scholar
  33. Wang C, Chen X, Xie G, Cao M (2017b) Emergence of leadership in a robotic fish group under diverging individual personality traits. R Soc Open Sci 4(5):161015CrossRefGoogle Scholar
  34. Yan W, Cui R, Xu D (2008) Formation control of underactuated autonomous underwater vehicles in horizontal plane. In: Proceedings of the IEEE international conference on automation and logistics, Qingdao, China, pp 822–827.  https://doi.org/10.1109/ICAL.2008.4636263
  35. Yan Z, Xu D, Chen T, Zhang W, Liu Y (2018) Leader-follower formation control of UUVs with model uncertainties, current disturbances, and unstable communication. Sensors 18(2):662CrossRefGoogle Scholar
  36. Yao Y, Xu D, Yan W (2009) Cooperative localization with communication delays for MAUVs. In: IEEE international conference on intelligent computing and intelligent systems, Shanghai, China. pp 244–249.  https://doi.org/10.1109/ICICISYS.2009.5357852
  37. Yu J, Wang C, Xie G (2016) Coordination of multiple robotic fish with applications to underwater robot competition. IEEE Trans Ind Electron 63(2):1280–1288CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yanlin He
    • 1
    • 2
    • 3
  • Lianqing Zhu
    • 1
    • 2
    Email author
  • Guangkai Sun
    • 1
    • 2
  • Junfei Qiao
    • 3
  • Shuxiang Guo
    • 4
  1. 1.Beijing Engineering Research Center of Optoelectronic Information and InstrumentsBeijing Information Science and Technology UniversityBeijingChina
  2. 2.Bionic and Intelligent Aerospace Vehicles LabBeijing Information Science and Technology UniversityBeijingChina
  3. 3.Beijing University of TechnologyBeijingChina
  4. 4.Beijing Institute of TechnologyBeijingChina

Personalised recommendations