Our main research interests include a range of topics in the emerging discipline of networked systems, which studies systems of intelligent physical agents interacting via a communication medium in search of local control principles that determine global network behavior. We focus on robotic, sensor, and wireless networks that can be used for a variety of tasks including remote sensing, environmental monitoring and mapping, reliable long-range communications, or distributed mobile computing, to name a few. We are particularly interested in hybrid, distributed, and robust solution techniques for integrated sensing, communication, computation, and control, that lie on the interface between control theory, distributed optimization, estimation, and networking. Our focus is on the theoretical challenges arising from such methods, but we are also interested in their experimental validation.
Graph Theoretic Connectivity Control of Mobile Robot Networks
Communication and network connectivity has emerged as one of the most important and critical requirements in numerous cooperative tasks, such as formation stabilization and consensus seeking problems. While the agents’ primary task is typically detection of certain physical changes within their proximity, their communication capabilities enable them to share the individually collected data with their peers, in order to achieve a global coordinated objective. Consequently, network connectivity has become a critical design specification of a network and, due to its global nature, distributed solutions are rather hard to obtain. The focus of this research is on distributed and provable algorithms that maintain connectivity in networks with dynamically changing communication topologies.
 Graph Theoretic Connectivity Control of Mobile Robot Networks. M. M. Zavlanos, M. B. Egerstedt, and G. J. Pappas. Proceedings of the IEEE: Special Issue on Swarming in Natural and Engineered Systems, Vol. 99, No. 9, pp. 1525-1540, September 2011.
 Distributed Connectivity Control of Mobile Networks. M. M. Zavlanos and G. J. Pappas. IEEE Transactions on Robotics, Vol. 24, No. 6, pp. 1416-1428, December 2008.
Distributed Multi-Robot Assignment and Placement
A fundamental yet poorly understood problem in multi-agent coordination is the assignment of multiple tasks to multiple agents. Most existing approaches decouple the assignment process from the execution process that follows any choice of task assignment. Despite the enormous combinatorial complexity of static, discrete assignment problems, in highly unpredictable environments, where the number of targets and agents dynamically change, dynamic assignment methods are clearly much more preferable. In addition to role assignment being dynamic, ideally one would also desire distributed coordination protocols for task assignment, as the best feasible way for addressing the computational complexity of the problem. The focus of this research is on distributed coordination algorithms for task assignment.
 A Distributed Auction Algorithm for the Assignment Problem. M. M. Zavlanos, L. Spesivtsev, and G. J. Pappas. Proc. 47th IEEE Conference on Decision and Control, Cancun, Mexico, December 2008, pp. 1212-1217.
 Dynamic Assignment in Distributed Motion Planning with Local Coordination. M. M. Zavlanos and G. J. Pappas. IEEE Transactions on Robotics, Vol. 24, No. 1, pp. 232-242, February 2008.
Identification of Gene Regulatory Networks in Molecular Biology
Gene regulatory networks capture the interactions between genes and other cellular components, resulting in the fundamental biological process of transcription and translation. In some applications, the topology of the regulatory network is not known, and has to be inferred from experimental data. The experimental data consist of expression levels of the genes, which are typically measured as mRNA concentrations in micro-array experiments. In a so called genetic perturbation experiment, small perturbations are applied to equilibrium states and the resulting changes in expression activity are measured. The focus of this research is on novel optimization techniques to identify genetic networks that explain data obtained from noisy genetic perturbation experiments or time course measurements. The impact of this research is not only in promoting biological knowledge, but also in drug discovery, where a systems-wide understanding of regulatory networks is crucial for identifying the targeted pathways.
 Inferring Stable Genetic Networks from Steady-State Data. M. M. Zavlanos, A. A. Julius, S. P. Boyd, and G. J. Pappas. Automatica Special Issue on Systems Biology, Vol. 47, No. 6, pp. 1113-1122, June 2011.
 Genetic Network Identification using Convex Programming. A. A. Julius, M. M. Zavlanos, S. P. Boyd, and G. J. Pappas. IET Systems Biology, Vol. 3, No. 3, pp. 155-166, May 2009.
Michael M. Zavlanos
Department of Mechanical Engneering & Materials Science
Michael M. Zavlanos | Last Update 07.14.2017
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