One of the main technical challenges facing intelligence analysts today is the efficient utilization of limited resources (both in quantity and capabilities) to maximize the accuracy and timeliness of collected data to address information gaps. The challenges on this problem increase when the communication between the information gathering assets is limited, preventing constant coordination of collection activities and information sharing among the assets. This work addresses the problem of routing cooperative autonomous vehicles (e.g., unmanned vehicles) operating in a dynamic environment to maximize overall information gain. Vehicles (collection assets) are collecting information on multiple objectives, subject to communication network constraints. In addition, this research studies the degradation of solution quality (i.e., information gain) as a centralized system synchronizing information gathering activities for a set of cooperative autonomous vehicles moves to a decentralized framework.
A mathematical programming model to determine routes in (de)centralized frameworks was developed. This model is based on a representation of potential information gain in discretized maps, the effectiveness of the assets collecting information and an obsolescence rate on the areas visited by the assets. The model assumes that information is only exchanged when assets are part of the same network, allowing a multi-perspective optimization of the information gathering activities in which each asset develops its own decisions based only on its perspective of the environment (i.e., potential information gain). This framework is used to evaluate the degradation of solution quality as a centralized system moves to a decentralized framework. This research extends the concept of ‘‘price of anarchy’’ (a measure on the inefficiency of a system when individuals (i.e., agents) maximize decisions without coordination) by considering different levels of decentralization.
Different communication network topologies are considered. Collection assets are part of the same communication network (i.e., a connected component) if: (1) a fully connected network exists between the assets in the connected component, or (2) a path (consisting of one or more communication links) between every asset in the connected component exists. Multiple connected components may exist among the available collection assets supporting a mission. Trust (with a suitable decay factor as a function of time) on the potential location of assets that are not part of a connected component is considered as part of an extension to the optimization model. A solution approach based on multiple aggregation strategies to obtain satisficing solutions that are computational efficient was developed.
|Advisor:||Sudit, Moises, Karwan, Mark H.|
|Commitee:||Hirsch, Michael, Nagi, Rakesh|
|School:||State University of New York at Buffalo|
|School Location:||United States -- New York|
|Source:||DAI-B 76/11(E), Dissertation Abstracts International|
|Subjects:||Applied Mathematics, Industrial engineering, Operations research|
|Keywords:||Communication network, Decentralization, Information gathering, Perspective optimization, Price of anarchy, Vehicle routing|
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