• control systems
• autonomous vehicles
• distributed systems

THIS thesis has the main goal of contribute to increase the reliability and robustness of a network of remotely or autonomous vehicles involved in monitoring and security applications in the maritime domain. The discussion tackles this topic modeling the network of marine vehicles as a complex system characterized by a mix of high and low level issues, in which high level operations rely on low-level ones and vice-versa.
The high level issues faced in this dissertation includes mission planning operations for a network of moving assets. The availability of heterogeneous data of different nature about the area of interest is exploited to generate risk maps. As first result, these produce an accurate situation awareness of the area of interest describing which sub-areas may require a deeper coverage. Then, the subsequent integration of such maps in planning system produces as result the execution of planning operations in a more effective way. This thesis presents the application of this approach in the scenario of counterpiracy operations.
Within the high level issues, this dissertation presents a systematic approach for the design and early assessment of the performance of a surveillance network for the protection of a high value asset against asymmetric threat. The problem is tackled through a game theoretic formalism as a potential game to incorporate in a mathematical representation the strategy pursued by surveillance operators during operative scenarios. The approach is evaluated through Monte Carlo simulations and performance of the approach is provided in terms of a security index that allows to obtain a tool for team sizing. The tool provides the minimum number of marine vehicles to be used in the system, given a desired security level to be guaranteed and the maximum threat velocity.
Finally, within low level issues, this thesis presents the simulative results and algorithmic developments of the task-priority based control applied to a distributed sampling network in an area coverage and adaptive sampling mission scenario. The proposed approach allows the fulfillment of a chain of tasks with decreasing priority each of which directly related to both operability and safety aspects of the entire mission. The task-priority control is presented both in the centralized and decentralized implementations showing a comparison of performance. Finally simulations of the adaptive sampling mission scenario are provided showing the effectiveness of the proposed solution.