Information-driven Cooperative Sampling Research By Using AUV Networks

This thesis addresses the problem of cooperative oceanic sampling using sensor-embedded Autonomous Underwater Vehicles(AUVs),which are being implemented for a multitude of scientific and commercial applications,such as marine biology research and marine resources exploration and development.The main motivation of driving multiple cooperative AUVs other than a single AUV is due to the emerging demands for the increasing complexity of such exploring tasks.The cooperative estimation and control of sensor-embedded AUV network system can show significant advantages on improving efficiency and robustness of the whole system,which lead to the study of cooperative sampling tasks and distributed control of multiple AUVs in this thesis.The main contributions of this thesis can be stated as follows:(1)A gradient scheme based on plume dynamics was introduced to the sampling tasks.A distributed estimator that allows the nodes of a sensor network to track the average of n sensor measurements was also provided.This consensus estimator plays a crucial role in solving a data fusion problem that allows implementation of a scheme for distributed data-driven filtering in sensor networks.Simulation results are provided to show the effectiveness of the theoretical results.(2)A rotating consensus algorithm for multi-agents was proposed by introducing a Cartesian Coordinate Coupling and a velocity consensus item to an existing consensus protocol.This note shows that the resulting rotating motions can be affected by network topology,the damping gain and the Euler angle.It is also shown that convergence of the rotating consensus can be achieved when the damping gain is above a certain bound,the Euler angle is equal to a critical value and the communication network topology has a directed spanning tree.In the case of rotating consensus tracking,it is shown that all the agents can rotating around a dynamic virtual leader,when this leader is a neighbor of only a subset of a group of followers and all followers have only local interaction.Tools like matrix theory,linear system theory,and other mathematical skills are used for convergence analysis.Simulation results are provided to illustrate the effectiveness of the theoretical results.(3)A distributed data-driven filter for movable sensor network was proposed to deal with the nonlinearity and complexity of the ocean dynamics.This algorithm shows that better performance can be achieved when movable sensors are used.Simulation are performed to investigate the effectiveness of the theory.