A distributed optimization framework for localization and formation control: applications to vision-based measurements
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CitationRoberto Tron, Justin Thomas, Giuseppe Loianno, Kostas Daniilidis, Vijay Kumar. 2016. "A distributed optimization framework for localization and formation control: applications to vision-based measurements." IEEE Control Systems, Volume 36, Issue 4, pp. 22 - 44.
Multiagent systems have been a major area of research for the last 15 years. This interest has been motivated by tasks that can be executed more rapidly in a collaborative manner or that are nearly impossible to carry out otherwise. To be effective, the agents need to have the notion of a common goal shared by the entire network (for instance, a desired formation) and individual control laws to realize the goal. The common goal is typically centralized, in the sense that it involves the state of all the agents at the same time. On the other hand, it is often desirable to have individual control laws that are distributed, in the sense that the desired action of an agent depends only on the measurements and states available at the node and at a small number of neighbors. This is an attractive quality because it implies an overall system that is modular and intrinsically more robust to communication delays and node failures.