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Distributed system of autonomous buoys for scalable deployment and monitoring of large waterbodies

  • Brandon M. Zoss
  • David Mateo
  • Yoke Kong Kuan
  • Grgur Tokić
  • Mohammadreza Chamanbaz
  • Louis Goh
  • Francesco Vallegra
  • Roland Bouffanais
  • Dick K. P. Yue
Article
Part of the following topical collections:
  1. Special Issue on Distributed Robotics: From Fundamentals to Applications

Abstract

The design, construction, and testing of a large distributed system of novel, small, low-cost, autonomous surface vehicles in the form of self-propelled buoys capable of operating in open waters is reported. We detail the successful testing of collective behaviors of systems with up to 50 buoys, achieving scalable deployment and dynamic monitoring in unstructured environments. This constitutes the largest distributed multi-robot system of its kind reported to date. We confirm the robustness of the system to the loss of multiple units for different collective behaviors such as flocking, navigation, and area coverage. For dynamic area monitoring, we introduce a new metric to quantify coverage effectiveness. Our system exhibits near optimal scalability for fixed target areas and a high degree of flexibility when the shape of the target changes with time. This system demonstrates the potential of distributed multi-robot systems for the pervasive and persistent monitoring of coastal and inland water environments.

Keywords

Multi-robot system Collective behavior Autonomous surface vehicle Dynamic area coverage Distributed communication 

Notes

Acknowledgements

This work was supported by Grants from the Temasek Lab (TL@SUTD) under a Seed Grant #IGDS S15 01021, a MOE-Tier 1 Grant #SUTDT12015003, and the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise programme. The Center for Environmental Sensing and Modeling is an interdisciplinary research group of the Singapore MIT Alliance for Research and Technology.

Supplementary material

References

  1. Alcântara, E. H., Stech, J. L., Lorenzzetti, J. A., Bonnet, M.-P., Casamitjana, X., Assireu, A. T., et al. (2010). Remote sensing of water surface temperature and heat flux over a tropical hydroelectric reservoir. Remote Sensing of Environment, 114(11), 2651–2665.CrossRefGoogle Scholar
  2. Applied Complexity Group. 51 networked buoys swarming, https://youtu.be/fhg1rIX_y3A.
  3. Applied Complexity Group. Dynamic area coverage (geofencing) field test, https://youtu.be/hlBNjHS_Q7s.
  4. Applied Complexity Group. Dynamic environmental monitoring using swarming mobile sensing buoys, https://youtu.be/Qe-wZOi3ONs.
  5. Applied Complexity Group. Field test—Dynamic area coverage: Flexibility experiment. https://youtu.be/KBEURgyPxXI.
  6. Applied Complexity Group. Field test—Dynamic area coverage: Scalability experiment, https://youtu.be/RPJSvC-X-Vs.
  7. Bayat, B., Crasta, N., Crespi, A., Pascoal, A. M., & Ijspeert, A. (2017). Environmental monitoring using autonomous vehicles: A survey of recent searching techniques. Current Opinion in Biotechnology, 45, 76–84.CrossRefGoogle Scholar
  8. Bayindir, L., & Şahin, E. (2007). A review of studies in swarm robotics. Turkish Journal of Electrical Engineering & Computer Sciences, 15(2), 115–147.Google Scholar
  9. Beşiktepe, Ş. T., Lermusiaux, P. F. J., & Robinson, A. R. (2003). Coupled physical and biogeochemical data-driven simulations of Massachusetts Bay in late summer: Real-time and postcruise data assimilation. Journal of Marine Systems, 40, 171–212.CrossRefGoogle Scholar
  10. Bohren, J., & Cousins, S. (2010). The SMACH high-level executive [ROS News]. IEEE Robotics Automation Magazine, 17, 18–20.CrossRefGoogle Scholar
  11. Bouffanais, R. (2016). Design and control of swarm dynamics Springer briefs in complexity. Singapore: Springer.CrossRefzbMATHGoogle Scholar
  12. Brambilla, M., Ferrante, E., Birattari, M., & Dorigo, M. (2013). Swarm robotics: A review from the swarm engineering perspective. Swarm Intelligence, 7(1), 1–41.CrossRefGoogle Scholar
  13. Chamanbaz, M., Mateo, D., Zoss, B. M., Tokić, G., Wilhelm, E., Bouffanais, R., et al. (2017). Swarm-enabling technology for multi-robot systems. Frontiers in Robotics and AI, 4, 12.CrossRefGoogle Scholar
  14. Costa, V., Duarte, M., Rodrigues, T., Oliveira, S. M., & Christensen, A. L. (2016). Design and development of an inexpensive aquatic swarm robotics system. In OCEANS 2016-Shanghai (pp. 1–7). IEEE.Google Scholar
  15. Couzin, I. D., Krause, J., Franks, N. R., & Levin, S. A. (2005). Effective leadership and decision-making in animal groups on the move. Nature, 433(7025), 513.CrossRefGoogle Scholar
  16. Curcio, J., McGillivary, P., Fall, K., Maffei, A., Schwehr, K., Twiggs, B., et al. (2006). Self-positioning smart buoys, the "un-buoy" solution: Logistic considerations using autonomous surface craft technology and improved communications infrastructure. In Proceedings of OCEANS 2006 (pp. 1–5).Google Scholar
  17. Duarte, M., Costa, V., Gomes, J., Rodrigues, T., Silva, F., Oliveira, S. M., et al. (2016). Evolution of collective behaviors for a real swarm of aquatic surface robots. PLoS ONE, 11(3), e0151834.CrossRefGoogle Scholar
  18. Duarte, M., Gomes, J., Costa, V., Rodrigues, T., Silva, F., Lobo, V., et al. (2016). Application of swarm robotics systems to marine environmental monitoring. In OCEANS 2016-Shanghai (pp. 1–8). IEEE.Google Scholar
  19. Fernández-Hermida, X., Durán-Neira, C., Lago-Reguera, M. D., Rodríguez-Alemparte, C., & Martín-Rodríguez, F. (2011). Hidroboya: An autonomous buoy for real time high quality sea and continental water data retrieval. In OCEANS 2011 IEEE-Spain (pp. 1–7). IEEE.Google Scholar
  20. Ferreira, H., Almeida, C., Martins, A., Almeida, J., Dias, N., Dias, A., et al. (2009). Autonomous bathymetry for risk assessment with ROAZ robotic surface vehicle. In OCEANS 2009-Europe (pp. 1–6). IEEE.Google Scholar
  21. Fine, Benjamin T., & Shell, Dylan A. (2013). Unifying microscopic flocking motion models for virtual, robotic, and biological flock members. Autonomous Robots, 35(2), 195–219.CrossRefGoogle Scholar
  22. Gage, D. W. (1992). Command control for many-robot systems. Technical report, DTIC Document.Google Scholar
  23. Home | Chesapeake Bay Interpretive Buoy System, http://buoybay.noaa.gov/.
  24. Howard, A., Matarić, M. J., & Sukhatme, G. S. (2002). Mobile sensor network deployment using potential fields: A distributed, scalable solution to the area coverage problem. In H. Asama, T. Arai, T. Fukuda, & T. Hasegawa (Eds.), Distributed Autonomous Robotic Systems 5 (pp. 299–308). Tokyo: Springer.Google Scholar
  25. Jadbabaie, A., Lin, J., & Morse, A. S. (2003). Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Transactions on Automatic Control, 48, 988–1001.MathSciNetCrossRefzbMATHGoogle Scholar
  26. Komareji, M., & Bouffanais, R. (2013). Resilience and controllability of dynamic collective behaviors. PLoS ONE, 8, e82578.CrossRefGoogle Scholar
  27. Kumar, P., Reddy, L., & Varma, S. H. (2009). Distance measurement and error estimation scheme for RSSI based localization in wireless sensor networks. In 2009 Fifth IEEE conference on wireless communication and sensor networks (WCSN) (pp. 1–4). IEEE.Google Scholar
  28. Leonard, N. E., Paley, D. A., Lekien, F., Sepulchre, R., Fratantoni, D. M., & Davis, R. E. (2007). Collective motion, sensor networks, and ocean sampling. Proceedings of the IEEE, 95(1), 48–74.CrossRefGoogle Scholar
  29. Madgwick, S., Harrison, A., & Vaidyanathan, R. (2011). Estimation of IMU and MARG orientation using a gradient descent algorithm. In 2011 IEEE International Conference on Rehabilitation Robotics (pp. 1–7). IEEE.Google Scholar
  30. Manley, J. E. (2008). Unmanned surface vehicles, 15 years of development. In OCEANS 2008 (pp. 1–4). IEEE.Google Scholar
  31. Matos, A., Almeida, R., & Cruz, N. (2016). Man portable acoustic navigation buoys. In Proceedings of OCEANS 2016-Shanghai (pp. 1–6).Google Scholar
  32. Murphy, R. R., Steimle, E., Hall, M., Lindemuth, M., Trejo, D., Hurlebaus, S., et al. (2011). Robot-assisted bridge inspection. Journal of Intelligent & Robotic Systems, 64(1), 77–95.CrossRefGoogle Scholar
  33. Nishida, Y., Kojima, J., Ito, Y., Tamura, K., Sugimatsu, H., Kim, K., et al. (2015). Development of an autonomous buoy system for AUV. In Proceedings of OCEANS 2015-Genova (pp. 1–6).Google Scholar
  34. Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007). Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE, 95(1), 215–233.CrossRefzbMATHGoogle Scholar
  35. Orton, P. M., McGillis, W. R., Moisan, J. R., Higinbotham, J. R., & Schirtzinger, C. (2009). The mobile buoy: An autonomous surface vehicle for integrated ocean-atmosphere studies. In AGU Spring Meeting Abstracts.Google Scholar
  36. Pico, G., Miranda, J., Marentes, K., & Tosunoglu, S. (2016). Multipurpose autonomous buoy. In Proceedings of the 29th Florida Conference on Recent Advances in Robotics, Miami, Florida (pp. 128–144).Google Scholar
  37. Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., et al. (2009). ROS: An open-source robot operating system. In ICRA workshop on open source software (Vol. 3).Google Scholar
  38. Ren, W., & Beard, R. (2008). Distributed consensus in multi-vehicle cooperative control. Berlin: Springer.CrossRefzbMATHGoogle Scholar
  39. Reynolds, C. W. (1987). Flocks, herds, and schools: A distributed behavioral model. Computer Graphics, 21, 25–34.CrossRefGoogle Scholar
  40. Robert, A. H. (2012). Infrastructure for large-scale tests in marine autonomy. Master’s thesis, Massachusetts Institute of Technology.Google Scholar
  41. Şahin, E. (2005). Swarm robotics: From sources of inspiration to domains of application. In E. Şahin, & W. M. Spears (Eds.), Swarm Robotics: SAB 2004 International Workshop Santa Monica, CA, USA, July 17, 2004 (pp. 10–20). Springer, Berlin.Google Scholar
  42. Schaap, W. E. (2007). The Delaunay tessellation field estimator. Ph.D. thesis, University of Gröningen.Google Scholar
  43. Srinivasan, R., Zacharia, S., Sudhakar, T., & Atmanand, M. A. (2016). Indigenous drifting buoys for the Indian ocean observations. In OCEANS 2016 MTS/IEEE Monterey (pp. 1–6). IEEE.Google Scholar
  44. Smart Buoys | Products | Pentair Environmental Sytems, http://www.pentairenvironmental.com/products/smart-buoys.html.
  45. Turgut, A. E., Çelikkanat, H., Gökçe, F., & Şahin, E. (2008). Self-organized flocking in mobile robot swarms. Swarm Intelligence, 2(2–4), 97–120.CrossRefGoogle Scholar
  46. Valada, A., Velagapudi, P., Kannan, B., Tomaszewski, C., Kantor, G., & Scerri, P. (2014). Development of a low cost multi-robot autonomous marine surface platform. In K. Yoshida, & S. Tadokoro (Eds.), Field and Service Robotics. Springer Tracts in Advanced Robotics (Vol. 92, pp. 643–658). Berlin: Springer.Google Scholar
  47. Vásárhelyi, G., Virágh, C., Somorjai, G., Tarcai, N., Szorenyi, T., Nepusz, T., et al. (2014). Outdoor flocking and formation flight with autonomous aerial robots. In Proceedings of 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3866–3873).Google Scholar
  48. Vesecky, J., Laws, K., Petersen, S., Bazeghi, C., & Wiberg, D. (2007a). Prototype autonomous mini-buoy for use in a wireless networked, ocean surface sensor array. In Proceedings of IEEE international geoscience and remote sensing symposium (pp. 4987–4990).Google Scholar
  49. Vesecky, J. F., Laws, K., Petersen, S. I., Bazeghi, C., & Wiberg, D. (2007b). Autonomous minibuoy prototype for a coordinated, wireless networked, ocean-surface-sensor array. In OCEANS 2007-Europe (pp. 1–5). IEEE.Google Scholar
  50. Vicsek, T., & Zafeiris, A. (2012). Collective motion. Physics Reports, 517, 71–140.CrossRefGoogle Scholar
  51. Virágh, C., Vásárhelyi, G., Tarcai, N., Szörényi, T., Somorjai, G., Nepusz, T., et al. (2014). Flocking algorithm for autonomous flying robots. Bioinspiration & Biomimetics, 9(2), 025012.CrossRefGoogle Scholar
  52. Ziccarelli, L., Dellor, R., Johnson, R., Schmitz, H., O’Reilly, T., & Chavez, F. (2016). A novel method of obtaining near real-time observations of phytoplankton from a mobile autonomous platform. In OCEANS 2016 MTS/IEEE Monterey (pp. 1–5). IEEE.Google Scholar
  53. Zoss, B. M. (2016). Design and analysis of mobile sensing systems: An environmental data collection swarm. Master’s thesis. Massachusetts Institute of Technology.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Massachusetts Institute of Technology (MIT)CambridgeUSA
  2. 2.Singapore University of Technology and Design (SUTD)SingaporeSingapore

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