1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;
2. The Second Institute of Oceanography, State Oceanic Administration, Laboratory of Submarine Geosciences, Hangzhou 310012, China
An adaptive coverage sampling algorithm is proposed for solving the dynamic and unknown ocean phenomenon by underwater gliders. Firstly, the criterion for optimal coverage sampling is defined based on the centroidal Voronoi partition of the sampling space. Secondly, recursive least square with forgetting factor is used to estimate the parameters of ocean phenomenon online. Finally, the distributed control law is proposed, which can guarantee the sampling network composed of underwater gliders to converge to the optimal sampling network config defined from the random initial state. The simulation experiment is carried out to show the effectiveness of the proposed method. The results show that the proposed adaptive coverage sampling strategy has a better performance in the sampling of dynamic ocean phenomenon.
 Zhu X K, Yu J C, Wang X H. Optimization of large scales ocean sampling for minimization of the Kriging variance[C]//8th World Congress on Intelligent Control and Automation. Piscataway, NJ, USA: IEEE, 2010: 7050-7054.
 朱心科,俞建成,王晓辉.能耗最优的水下滑翔机采样路径规划[J].机器人,2011,33(3): 360-365. Zhu X K, Yu J C, Wang X H. Sampling path planning of optimal energy consumption for underwater glider[J]. Robot, 2011, 33(3): 360-365.
 Li W, Cassandras C G. Distributed cooperative coverage control of sensor networks[C]//44th IEEE Conference on Decision and Control. Piscataway, NJ, USA: IEEE, 2005: 2542-2547.
 Zhao F, Shin J, Reich J. Information-driven dynamic sensor collaboration[J]. IEEE Signal Processing Magazine, 2002, 19(2): 61-72.
 Krause A, Guestrin C, Gupta A, et al. Near-optimal sensor placements: Maximizing information while minimizing communication cost[C]//5th Information Processing in Sensor Networks. New York, NY, USA: ACM, 2006: 1-10.
 González-Banos H. A randomized art-gallery algorithm for sensor placement[C]//17th Annual ACM Symposium on Computional Geometry. New York, NY, USA: ACM, 2001: 232-240.
 Fiorelli E, Leonard N E, Bhatta P. Multi-AUV control and adaptive sampling in Monterey Bay[C]//IEEE/OES Autonomous Underwater Vehicles Conference. Piscataway, NJ, USA: IEEE, 2004: 134-147.
 Cortes J, Martinez S, Karatas T, et al. Coverage control for mobile sensing networks[J]. IEEE Transactions on Robotics and Automation, 2004, 20(2): 243-255.
 Schwager M, Bullo F, Skelly D, et al. A ladybug exploration strategy for distributed adaptive coverage control[C]//IEEE International Conference on Robotics and Automation. Piscataway, NJ, USA: IEEE, 2008: 2346-2353.
 Schwager M, Slotine J, Rus D. Decentralized, adaptive control for coverage with networked robots[C]//IEEE International Conference on Robotics and Automation. Piscataway, NJ, USA: IEEE, 2007: 3289-3294.
 Schwager M, McLurkin J, Rus D. Distributed coverage control with sensory feedback for networked robots[C]//Proceedings of Robotics: Science and Systems. Cambridge, MA, USA: MIT Press, 2006: 1-8.
 Du Q, Faber V, Gunzburger M. Centroidal Voronoi tessellations: applicataions and algorithms[J]. SLAM Review, 1999, 41(4): 637-676.
 Ljung L.系统辨识:使用者的理论[M].2版.北京:清华大学出版社,2003: 363-364. Ljung L. System identification: Theory for the user[M]. 2nd ed. Beijing: Tsinghua University Press, 2003:363-364.
 Slotine J J, Li W P. Applied nonlinear control[M]. Englewood Cliffs, NJ, USA: Prentice Hall, 1991: 358-376.
 Schwager M. A gradient optimization approach to adaptive multi-robot control[D]. Cambridge, MA, USA: MIT Press, 2009: 71-97.