Autonomous Robots

, Volume 43, Issue 6, pp 1555–1574 | Cite as

Advancing multi-vehicle deployments in oceanographic field experiments

  • António Sérgio FerreiraEmail author
  • Maria Costa
  • Frédéric Py
  • José Pinto
  • Mónica A. Silva
  • Alex Nimmo-Smith
  • Tor Arne Johansen
  • João Borges de Sousa
  • Kanna Rajan


Our research concerns the coordination and control of robotic vehicles for upper water-column oceanographic observations. In such an environment, operating multiple vehicles to observe dynamic oceanographic phenomena, such as ocean processes and marine life, from fronts to cetaceans, has required that we design, implement and operate software, methods and processes which can support opportunistic needs in real-world settings with substantial constraints. In this work, an approach for coordinated measurements using such platforms, which relate directly to task outcomes, is presented. We show the use and operational value of a new Artificial Intelligence based mixed-initiative system for handling multiple platforms along with the networked infrastructure support needed to conduct such operations in the open sea. We articulate the need and use of a range of middleware architectures, critical for such deployments and ground this in the context of a field experiment in open waters of the mid-Atlantic in the summer of 2015.


Mixed-initiative control Marine robotics Control Artificial Intelligence Ocean science Operational oceanography Upper water-column biology 



Johansen was supported in part by the Research Council of Norway through the Centers of Excellence funding scheme, grant number 223254 – NTNU AMOS, and by grant number 235348. Rajan and Py were supported by the US Office of Naval Research, ONR Grant # N00014-14-1-0536. Silva was supported by FEDER and COMPETE funds and by FCT grant # IF/00943/2013. Silva was also supported by FRCT from the Government of the Açores. Authors are grateful to the Cetacean Ecology Research Group including Pablo Chellavard Navarro, Rui Prieto, Cláudia Oliveira, Marta Tobena, and skipper Renato Bettencourt, and to NTNU’s UAV team, including Lars Semb, Krzysztof Cisek, Frederik Leira and João Fortuna. This work was partially supported by the SUNRISE - “Sensing, monitoring and actuating on the UNderwater world through a federated Research InfraStructure Extending the Future Internet”, project #611449, funded by the European Union’s Seventh Framework Programme for Research and Technological Development - Large scale integrating project (IP). We are grateful to the captain and crew of NRP Gago Coutinho. Finally, we are grateful to the entire ls team that participated in the rp exercise in the Açores.


  1. Ai-Chang, M., Bresina, J., Charest, L., Chase, A., Hsu, J., Jonsson, A., et al. (2004). MAPGEN: Mixed initiative planning and scheduling for the Mars’03 MER Mission. IEEE Intelligent Systems, 19(1), 8–12.CrossRefGoogle Scholar
  2. Arrichiello, F., Das, J., Heidarsson, H. K., Sukhatme, G. S., & Chiaverini, S. (2009). Experiments in autonomous navigation with an under-actuated surface vessel via the Null-Space based Behavioral control. In Conference on advanced intelligent mechatronics (pp. 362–367).Google Scholar
  3. Benjamin, M. R., Schmidt, H., Newman, P. M., & Leonard, J. J. (2010). Nested autonomy for unmanned marine vehicles with MOOS-IvP. Journal of Field Robotics, 27(6), 834–875.CrossRefGoogle Scholar
  4. Bernard, D. E., Dorais, G. A., Fry, C., Jr., E. G., Kanfesky, B., Kurien, J., et al. (1998). Design of the Remote Agent experiment for spacecraft autonomy. In Proceedings of the IEEE aerospace conference. Snowmass, CO.Google Scholar
  5. Block, B. A., Jonsen, I. D., Jorgensen, S. J., Winship, A. J., Shaffer, S. A., Bograd, S. J., et al. (2011). Tracking apex marine predator movements in a dynamic ocean. Nature, 475(7354), 86–90. Scholar
  6. Bresina, J., Jonsson, A., Morris, P., & Rajan, K. (2005). Activity planning for the mars exploration rovers. In International conference on automated planning and scheduling (ICAPS). Monterey, CA.Google Scholar
  7. Bresina, J., Jonsson, A., Morris, P., & Rajan, K. (2005). Mixed-initiative planning in MAPGEN: capabilities and shortcomings. In Proceedings of the workshop on mixed-initiative planning and scheduling, ICAPS. Monterey, CA.Google Scholar
  8. Brooks, R. (1986). A robust layered control system for a mobile robot. IEEE Journal on Robotics and Automation, 2(1), 14–23.CrossRefGoogle Scholar
  9. Chrpa, L., Pinto, J., Ribeiro, M., Py, F., Sousa, J., & Rajan, K. (2015). On mixed-initiative planning and control for autonomous underwater vehicles. In IEEE/RSJ international conference on intelligent robots and systems (IROS). Hamburg, Germany.Google Scholar
  10. Curtin, T. B., Bellingham, J. G., Catipovic, J., & Webb, D. (1993). Autonomous oceanographic sampling networks. Oceanography, 6(3), 86–94.CrossRefGoogle Scholar
  11. Das, J., Maughan, T., McCann, M., Godin, M., O’Reilly, T., Messie, M., Bahr, F., et al. (2011). Towards mixed-initiative, multi-robot field experiments: Design, deployment, and lessons learned. In Proceedings of the intelligent robots and systems (IROS). San Francisco, CA.Google Scholar
  12. Das, J., Py, F., Maughan, T., Messie, M., O’Reilly, T., Ryan, J., et al. (2012). Coordinated sampling of dynamic oceanographic features with AUVs and drifters. International Journal of Robotics Research, 31, 626–646.CrossRefGoogle Scholar
  13. Das, J., Rajan, K., Frolov, S., Ryan, J. P., Py, F., Caron, D. A., et al. (2010). Towards marine bloom trajectory prediction for AUV mission planning. In IEEE international conference on robotics and automation (ICRA). Anchorage, Alaska.Google Scholar
  14. de Sousa, J. B., Pereira, J., Alves, J., Galocha, M., Pereira, B., Lourenço, P., et al. (2015). Experiments in multi-vehicle operations: The rapid environmental picture atlantic exercise 2014. In OCEANS 2015—Genova (pp. 1–7). IEEE.
  15. Dias, P. S., Fraga, S. L., Gomes, R. M., Goncalves, G. M., Pereira, F. L., Pinto, J., et al. (2005). NEPTUS—A framework to support multiple vehicle operation. In Proceedings of the oceans MTS/IEEE conference (pp. 963–968). Brest, France.Google Scholar
  16. Dukeman, A., Adams, J. A., & Edmondson, J. (2016). Extensible collaborative autonomy using GAMS. In Proceedings of the 31st annual ACM symposium on applied computing (pp. 281–283). ACM.Google Scholar
  17. Edmondson, J., & Schmidt, D. (2010). Multi-agent distributed adaptive resource allocation (madara). International Journal of Communication Networks and Distributed Systems, 5(3), 229–245.CrossRefGoogle Scholar
  18. Encarnaçao, P., & Pascoal, A. (2001). Combined trajectory tracking and path following: An application to the coordinated control of autonomous marine craft. In: Proceedings of the 40th IEEE conference on decision and control, 2001 (Vol. 1, pp. 964–969). IEEE.Google Scholar
  19. Faria, M., Pinto, J., Py, F., Fortuna, J., Dias, H., Leira, R. M. F., et al. (2014). Coordinating UAVs and AUVs for oceanographic field experiments: Challenges and lessons learned. In IEEE international conference on robotics and automation (ICRA). Hong Kong.Google Scholar
  20. Fedak, M. (2004). Marine animals as platforms for oceanographic sampling: a “win/win” situation for biology and operational oceanography. Memoirs of the National Institute for Polar Research, Special Issue, 58, 133–147.Google Scholar
  21. Ferreira, A. S., Pinto, J., Sousa Dias, P., & de Sousa, J. B. (2017). The LSTS software tool chain for persistent maritime operations applied through vehicular ad-hoc networks. In 2017 International conference on unmanned aircraft systems (ICUAS) (pp. 609–616). IEEE.Google Scholar
  22. Fikes, R., & Nilsson, N. (1971). STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2, 189–205.CrossRefzbMATHGoogle Scholar
  23. Frank, J., & Jónsson, A. (2003). Constraint-based attribute and interval planning. Constraints, 8(4), 339–364.MathSciNetCrossRefzbMATHGoogle Scholar
  24. Ghallab, M., Nau, D., & Traverso, P. (2016). Automated planning and acting. Cambridge: Cambridge University Press.zbMATHGoogle Scholar
  25. Gonçalves, G., Gonçalves, R., Ferreira, A. S., de Sousa, J. B., & Pinto, J. (2012). Supervisory control: Optimal distribution of workload among operators for mixed initiative control of multiple UAVs. In L. van Breda (Ed.), Supervisory control of multiple uninhabited systems—Methodologies and enabling human–robot interface technologies, chap. 10 (pp. 153–163). Neuilly sur Seine: STO/NATO.Google Scholar
  26. Graham, G. W., & Nimmo Smith, W. A. M. (2010). The application of holography to the analysis of size and settling velocity of suspended cohesive sediments. Limnology and Oceanography: Methods, 8(1), 1–15.Google Scholar
  27. Jónsson, A., Morris, P., Muscettola, N., Rajan, K., & Smith, B. (2000). Planning in interplanetary space: Theory and practice. In Artificial intelligence planning and scheduling (AIPS).Google Scholar
  28. Lapierre, L., Soetanto, D., & Pascoal, A. (2003). Coordinated motion control of marine robots. In Proceedings of the 6th IFAC MCMC, Girona, Spain.Google Scholar
  29. Leira, F. S., Johansen, T. A., & Fossen, T. I. (2015). Automatic detection, classification and tracking of objects in the ocean surface from UAVS using a thermal camera. In 2015 IEEE aerospace conference (pp. 1–10).
  30. Lemai-Chenevier, S., & Ingrand, F. (2004). Interleaving temporal planning and execution in robotics domains. In Association for the advancement of Artificial Intelligence, National Conference (AAAI).Google Scholar
  31. Ghallab, M., Nau, D., & Traverso, P. (2004). Automated planning: Theory and practice. Amsterdam: Elsevier Science.zbMATHGoogle Scholar
  32. Manley, J., & Willcox, S. (2010). The wave glider: A persistent platform for ocean science. In OCEANS 2010 IEEE-Sydney (pp. 1–5). IEEE.Google Scholar
  33. Martinez, C., & Keener-Chavis, P. (2006). NOAA Ship Okeanos Explorer: Telepresence in the service of science, education and outreach. In: OCEANS 2006 (pp. 1–5). IEEE.Google Scholar
  34. Martins, R., & de Sousa, J. B. (2013). An extensible networking architecture for autonomous underwater vehicles. In: 2013 OCEANS—San Diego (pp. 1–5).Google Scholar
  35. Martins, R., de Sousa, J. B., Afonso, C. C., & Incze, M. (2011). REP10 AUV: Shallow water operations with heterogeneous autonomous vehicles. In OCEANS 2011 IEEE—Spain (pp. 1–6). IEEE.
  36. McGann, C., Py, F., Rajan, K., Thomas, H., Henthorn, R., & McEwen, R. (2008). A deliberative architecture for AUV control. In IEEE international conference on robotics and automation (ICRA). Pasadena.Google Scholar
  37. McGillivary, P., Sousa, J., Martins, R., Rajan, K., & Leroy, F. (2012). Integrating autonomous underwater vessels, surface vessels and aircraft as persistent surveillance components of ocean observing studies. In IEEE autonomous underwater vehicles, Southampton, UK.Google Scholar
  38. Morato, T., Varkey, D. A., Damaso, C., Machete, M., Santos, M., Prieto, R., et al. (2008). Evidence of a seamount effect on aggregating visitors. Marine Ecology Progress Series, 357, 23–32.CrossRefGoogle Scholar
  39. Muscettola, N., Nayak, P., Pell, B., & Williams, B. (1998). Remote agent: To boldly go where no AI system has gone before. Artificial Intelligence, 103, 5–48.CrossRefzbMATHGoogle Scholar
  40. Newman, P. (2003). A mission oriented operating suite. Technical report, Technical Report OE2007-07. MIT Department of Ocean Engineering.Google Scholar
  41. Pinto, J., Faria, M., Fortuna, J., Martins, R., Sousa, J., Queiroz, N., et al. (2013). Chasing fish: Tracking and control in a autonomous multi-vehicle real-world experiment. In MTS/IEEE oceans, San Diego, CA.Google Scholar
  42. Pinto, J., Martins, P. S. D. R., Fortuna, J., Marques, E., & Sousa, J. (2013). The LSTS tool chain for networked vehicle systems. In MTS/IEEE oceans (pp. 1–9). IEEE.Google Scholar
  43. Pinto, J. Q., Dias, P. S., Gonçalves, R., Gonçalves, G. M., Sousa, J. T. F. B., Pereira, F. L., et al. (2006). Neptus a framework to support a mission life cycle. In Proceedings of the 7th conference on manoeuvring and control of marine craft.Google Scholar
  44. Py, F., Rajan, K., & McGann, C. (2010). A systematic agent framework for situated autonomous systems. In 9th International conference on autonomous agents and multiagent systems (AAMAS). Toronto, Canada.Google Scholar
  45. 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, p. 5). Kobe.Google Scholar
  46. Rajan, K., Bernard, D., Dorais, G., Gamble, E., Kanefsky, B., Kurien, J., et al. (2000). Remote agent: An autonomous control system for the new millennium. In Proceedings prestigious applications of intelligent systems, European conference on AI, BerlinGoogle Scholar
  47. Rajan, K., & Py, F. (2012). T-REX: Partitioned inference for AUV mission control. In G. N. Roberts & R. Sutton (Eds.), Further advances in unmanned marine vehicles (pp. 171–199). Stevenage: The Institution of Engineering and Technology (IET).Google Scholar
  48. Rajan, K., Py, F., & Berreiro, J. (2012). Towards deliberative control in marine robotics. In M. Seto (Ed.), Autonomy in marine robots (pp. 91–175). Berlin: Springer.Google Scholar
  49. Ramp, S. R., Davis, R. E., Leonard, N. E., Shulman, I., Chao, Y., Robinson, A., et al. (2009). Preparing to predict: The second autonomous ocean sampling network (AOSN-II) experiment in the Monterey Bay. Deep Sea Research Part II: Topical Studies in Oceanography, 56(3), 68–86.CrossRefGoogle Scholar
  50. Roberts, J. J., Best, B. D., Mannocci, L., Fujioka, E., Halpin, P. N., Palka, D. L., et al. (2016). Habitat-based cetacean density models for the U.S. Atlantic and Gulf of Mexico. Scientific Reports, 6(1), 22615. Scholar
  51. Shkurti, F., Xu, A., Meghjani, M., Higuera, J. C. G., Girdhar, Y., Giguere, P., et al. (2012). Multi-domain monitoring of marine environments using a heterogeneous robot team. In 2012 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1747–1753). IEEE.Google Scholar
  52. Sousa, A., Madureira, L., Coelho, J., Pinto, J., Pereira, J., Sousa, J., et al. (2012). LAUV: The man-portable autonomous underwater vehicle. In F. Lobo Pereira (Ed.), Navigation, guidance and control of underwater vehicles (pp. 268–274). Porto, Portugal: IFAC.Google Scholar
  53. Sousa, L. L., López-Castejón, F., Gilabert, J., Relvas, P., Couto, A., Queiroz, N., et al. (2016). Integrated monitoring of mola mola behaviour in space and time. PLoS One.
  54. Stoker, C. R., Burch, D., Hine, B. P., & Barry, J. (1995). Antarctic undersea exploration using a robotic submarine with a telepresence user interface. IEEE Expert, 10(6), 14–23.CrossRefGoogle Scholar
  55. Webb, D. C., Simonetti, P. J., & Jones, C. P. (2001). Slocum: An underwater glider propelled by environmental energy. IEEE Journal of oceanic engineering, 26(4), 447–452.CrossRefGoogle Scholar
  56. Yen, P. P., Sydeman, W. J., & Hyrenbach, K. D. (2004). Marine bird and cetacean associations with bathymetric habitats and shallow-water topographies: Implications for trophic transfer and conservation. Journal of Marine systems, 50(1), 79–99.CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • António Sérgio Ferreira
    • 1
    Email author
  • Maria Costa
    • 1
  • Frédéric Py
    • 1
    • 5
  • José Pinto
    • 1
  • Mónica A. Silva
    • 2
  • Alex Nimmo-Smith
    • 3
  • Tor Arne Johansen
    • 4
  • João Borges de Sousa
    • 1
  • Kanna Rajan
    • 1
    • 4
  1. 1.Faculdade de Engenharia da Universidade do PortoPortoPortugal
  2. 2.IMAR-Açores & MAREMarine and Environmental Sciences CenterHortaPortugal
  3. 3.School of Biological and Marine SciencesUniversity of PlymouthPlymouthUK
  4. 4.Center for Autonomous Marine Operations and Systems, Department of Engineering CyberneticsNorwegian University of Science and Technology (NTNU)TrondheimNorway
  5. 5.SINTEF DigitalTrondheimNorway

Personalised recommendations