Autonomous underwater vehicle teams for adaptive ocean sampling: a data-driven approach
- 476 Downloads
The current technological developments in autonomous underwater vehicles (AUVs) and underwater communication have nowadays allowed to push the original idea of autonomous ocean sampling network even further, with the possibility of using each agent of the network not only as an operative component driven by external commands (model-driven) but as a reactive element able to act in response to changing conditions as measured during the exploration (data-driven). With this paper, we propose a novel data-driven algorithm for AUVs team for adaptive sampling of oceanic regions, where each agent shares its knowledge of the environment with its teammates and autonomously takes decision in order to reconstruct the desired oceanic field. In particular, sampling point selection is made in order to minimize the uncertainty in the estimated field while keeping communication contact with the rest of the team and avoiding to repeatedly sampling sub-regions already explored. The proposed approach is based on the use of the emergent behaviour technique and on the use of artificial potential functions (interest functions) to achieve the desired goal at the end of the mission. In this way, there is no explicit minimization of a cost functional at each decision step. The oceanic field is reconstructed by the application of radial basis functions interpolation of irregularly spaced data. A simulative example for the estimation of a salinity field with sea data obtained using the Mediterranean Sea Forecasting System is shown in the paper, in order to investigate the effect of the different uncertainty sources, including sea currents, on the behaviour of the exploration team and ultimately on the reconstruction of the salinity field.
KeywordsAutonomous underwater vehicles (AUVs) Vehicles cooperation Data-driven approach Ocean sampling Autonomous ocean sampling networks
The authors are grateful to the anonymous reviewers for their constructive criticisms and suggestions. This work was supported in part by European Union, 7th Framework Programme, Project UAN—Underwater Acoustic Network under Grant no. 225669 and Project Co^3 AUV—Cognitive Cooperative Control for Autonomous Underwater Vehicles", Grant n. IST-231378.
- Anderson B, and Crowell J (2005) Workhorse AUV – A cost-sensible new Autonomous Underwater Vehicle for Surveys/Soundings, Search & Rescue, and Research, In Proc. IEEE Oceans’05 ConferenceGoogle Scholar
- Caiti A, Crisostomi E, Munafò A (2009) “Physical characterization of acoustic communication channel properties in underwater mobile sensor networks”, Underwater Mobile Sensor Networks Sensors Systems and Software, Springer Berlin, ISBN978-3-642-11527-1, pp. 121–126Google Scholar
- Curtin T, Bellingham J, Catopovic J, Webb D (1993) Autonomous oceanographic sampling networks. Oceanography 6(3):86–94Google Scholar
- Martinez S, Cortez J, Bullo F (2007) “Motion coordination with distributed information”, IEEE Control System Magazine, pp. 75–88, August 2007Google Scholar
- Mediterranean forecasting system towards environmental predictions, available online at: mfstep.bo.ingv.it. Accessed by January 15, 2011.Google Scholar
- Schaback R (1997) “Reconstruction of multivariate functions from scattered data”, available on line at: http://www.num.math.uni-goettingen.de/schaback/research/group.html. Accessed by April 5, 2011.
- Tanner HG, Jabadaie A, Pappas GJ (2003) “Stable flocking of mobile agents, part I: fixed topology”, Proc. 42nd IEEE Conference on Decisions and ControlGoogle Scholar
- Schaback R (1995) “Multivariate interpolation and approximation by translates of a radial basis function”, Approximation Theory VIII C.K.Chui, L. L. Schumaker (eds.), World Scientific Publishing Co., 1995Google Scholar
- Iske A (2003) “Radial basis function: basics, advanced topics and meshfree methods for transport problem”, Spline and radial basis function, vol. 61, n. 3, Rend. Sem. Mat. Univ. Pol. Torino, Italy, 2003Google Scholar