Environmental Monitoring Using Autonomous Aquatic Robots: Sampling Algorithms and Experiments

This brief presents a practical solution to the problem of monitoring an environmental process in a large region by a small number of robotic sensors. Optimal sampling strategies are developed, taking into account the quality of the estimated environmental field and the lifetime of the sensors. We also present experimental results for monitoring a temperature field of an outdoor swimming pool sampled by an autonomous aquatic surface robot. Simulation and experimental results are provided to validate the proposed scheme.

[1]  D. Delchamps,et al.  Analytic stabilization and the algebraic Riccati equation , 1983, The 22nd IEEE Conference on Decision and Control.

[2]  Christopher K. Wikle,et al.  Space-time Kalman filter , 2014 .

[3]  John W. Fisher,et al.  Approximate Dynamic Programming for Communication-Constrained Sensor Network Management , 2007, IEEE Transactions on Signal Processing.

[4]  Jongeun Choi,et al.  Distributed learning and cooperative control for multi-agent systems , 2009, Autom..

[5]  Mark J. L. Orr,et al.  Regularization in the Selection of Radial Basis Function Centers , 1995, Neural Computation.

[6]  H. Akaike A new look at the statistical model identification , 1974 .

[7]  Randy A. Freeman,et al.  Decentralized Environmental Modeling by Mobile Sensor Networks , 2008, IEEE Transactions on Robotics.

[8]  Kian Hsiang Low,et al.  Robot Boats as a Mobile Aquatic Sensor Network , 2009 .

[9]  R. Shumway,et al.  AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM , 1982 .

[10]  R. Horowitz,et al.  Analysis of Discrete-Time H2 Guaranteed Cost Performance , 2009 .

[11]  A. Laub,et al.  Generalized eigenproblem algorithms and software for algebraic Riccati equations , 1984, Proceedings of the IEEE.

[12]  Han-Lim Choi,et al.  Continuous trajectory planning of mobile sensors for informative forecasting , 2010, Autom..

[13]  Jongeun Choi,et al.  Mobile Sensor Network Navigation Using Gaussian Processes With Truncated Observations , 2011, IEEE Transactions on Robotics.

[14]  Naomi Ehrich Leonard,et al.  Collective Motion, Sensor Networks, and Ocean Sampling , 2007, Proceedings of the IEEE.

[15]  Joonho Lee,et al.  Swarm intelligence for achieving the global maximum using spatio-temporal Gaussian processes , 2008, 2008 American Control Conference.