Multi-Agent Distributed Estimation
نویسنده
چکیده
The knowledge about its operation environment is a fundamental requirement for intelligent agent acting in a dynamic world. Knowledge gathering is thus a critical functionality for any agents and it involves several problems: perception, object detection, environment structure reconstruction and so on. The state estimation problem is a general definition to describe many applications: object tracking, localization, mapping, exploration. The core concept is that the agent has to operate in an environment, basing its actions on what it thinks about the environment. The “state estimation problem” has this goal: given some kind of sensations, it reconstructs a model of the world. Sometimes an agent can have an idea “a priori” about how its world is made. But often it does not know anything and has to also build a model on data. While a lot of researches has been done in the past about single-agent state estimation problem, in recent years, a lot of researchers focused their attention about exploiting state estimation technique using multiple agents. Using several agents gives more challenging questions. For example, should agents share their own information or not? How deal with communication problems? And what about scalability of methods? The Data Fusion model was developed by the Joint Directors of Laboratories Data Fusion Group, a US DoD government committee overseeing US defense technology. The stated purpose for that model and its subsequent revision has been
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تاریخ انتشار 2007