Zavlanos, Michael M
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Publication Distributed Topology Control of Dynamic Networks(2008-06-11) Zavlanos, Michael M; Tahbaz-Salehi, Alireza; Jadbabaie, Ali; Pappas, George JIn this paper, we present a distributed control framework for controlling the topology of dynamic multi-agent networks. Agents are equipped with local sensing and wireless communication capabilities, however, due to power constraints, they are required to switch between two modes of operation, namely active and sleep. The control objective investigated in this paper is to determine distributed coordination protocols that regulate switching between the operation modes of every agent such that the overall network guarantees multi-hop communication links among a subset of so called boundary agents. In the proposed framework, coordination is based on a virtual market where every request to switch off is associated with a bid. Combinations of requests are verified with respect to connectivity and the one corresponding to the highest aggregate bid is finally served. Other than nearest neighbor information, our approach assumes no knowledge of the network topology, while verification of connectivity relies on notions of algebraic graph theory as well as gossip algorithms run over the network. Integration of the individual controllers results in an asynchronous networked control system for which we show that it satisfies the connectivity specification almost surely. We finally illustrate efficiency of our scalable approach in nontrivial computer simulations.Publication Genetic network identification using convex programming(2009-05-19) Zavlanos, Michael; Julius, A; Pappas, George J; Boyd, SPublication Maintaining Connectivity in Mobile Robot Networks(2009-03-28) Michael, Nathan; Zavlanos, Michael M; Kumar, Vijay; Pappas, George JWhile there has been significant progress in recent years in the study of estimation and control of dynamic network graphs, limited attention has been paid to the experimental validation and verification of such algorithms on distributed teams of robots. In this work we conduct an experimental study of a non-trivial distributed connectivity control algorithm on a team of seven nonholonomic robots as well as in simulation. The implementation of the algorithm is completely decentralized and asynchronous, assuming that each robot only has access to its pose and knowledge of the total number of robots. All other necessary information is determined via message passing with neighboring robots. We show that such algorithms, requiring complex inter-agent communication and coordination, are feasible as well as highly successful in enabling a network of robots to adapt to disturbances while preserving connectivity.Publication A Distributed Auction Algorithm for the Assignment Problem(2008-12-09) Zavlanos, Michael M; Spesivtsev, Leonid; Pappas, George JThe assignment problem constitutes one of the fundamental problems in the context of linear programming. Besides its theoretical significance, its frequent appearance in the areas of distributed control and facility allocation, where the problemspsila size and the cost for global computation and information can be highly prohibitive, gives rise to the need for local solutions that dynamically assign distinct agents to distinct tasks, while maximizing the total assignment benefit. In this paper, we consider the linear assignment problem in the context of networked systems, where the main challenge is dealing with the lack of global information due to the limited communication capabilities of the agents. We address this challenge by means of a distributed auction algorithm, where the agents are able to bid for the task to which they wish to be assigned. The desired assignment relies on an appropriate selection of bids that determine the prices of the tasks and render them more or less attractive for the agents to bid for. Up to date pricing information, necessary for accurate bidding, can be obtained in a multi-hop fashion by means of local communication between adjacent agents. Our algorithm is an extension to the parallel auction algorithm proposed by Bertsekas et al to the case where only local information is available and it is shown to always converge to an assignment that maximizes the total assignment benefit within a linear approximation of the optimal one.Publication Identification of stable genetic networks using convex programming(2008-06-11) Zavlanos, Michael M; Julius, A. Agung; Pappas, George J; Boyd, Stephen PGene regulatory networks capture interactions between genes and other cell substances, resulting in various models for the fundamental biological process of transcription and translation. The expression levels of the genes are typically measured in 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. This paper develops a novel algorithm that identifies a sparse stable genetic network that explains noisy genetic perturbation experiments obtained at equilibrium. Our identification algorithm can also incorporate a variety of possible prior knowledge of the network structure, which can be either qualitative, specifying positive, negative or no interactions between genes, or quantitative, specifying a range of interaction strength. Our method is based on a convex programming relaxation for handling the sparsity constraint, and therefore is applicable to the identification of genome-scale genetic networks.Publication Distributed Connectivity Control of Mobile Networks(2008-12-20) Zavlanos, Michael M; Pappas, George JControl of mobile networks raises fundamental and novel problems in controlling the structure of the resulting dynamic graphs. In particular, in applications involving mobile sensor networks and multiagent systems, a great new challenge is the development of distributed motion algorithms that guarantee connectivity of the overall network. Motivated by the inherently discrete nature of graphs as combinatorial objects, we address this challenge using a key control decomposition. First, connectivity control of the network structure is performed in the discrete space of graphs and relies on local estimates of the network topology used, along with algebraic graph theory, to verify link deletions with respect to connectivity. Tie breaking, when multiple such link deletions can violate connectivity, is achieved by means of gossip algorithms and distributed market-based control. Second, motion control is performed in the continuous configuration space, where nearest-neighbor potential fields are used to maintain existing links in the network. Integration of the earlier controllers results in a distributed, multiagent, hybrid system, for which we show that the resulting motion always ensures connectivity of the network, while it reconfigures toward certain secondary objectives. Our approach can also account for communication time delays as well as collision avoidance and is illustrated in nontrivial computer simulations.Publication Controlling Connectivity of Dynamic Graphs(2005-12-01) Zavlanos, Michael M; Pappas, George JThe control of mobile networks of multiple agents raises fundamental and novel problems in controlling the structure of the resulting dynamic graphs. In this paper, we consider the problem of controlling a network of agents so that the resulting motion always preserves various connectivity properties. In particular, we consider preserving k-hop connectivity, where agents are allowed to move while maintaining connections to agents that are no more than k-hops away. The connectivity constraint is translated to constrains on individual agent motion by considering the dynamics of the adjacency matrix and related constructs from algebraic graph theory. As special cases, we obtain motion constraints that can preserve the exact structure of the initial dynamic graph, or may simply preserve the usual notion connectivity while the structure of the graph changes over time. We conclude by illustrating various interesting problems that can be achieved while preserving connectivity constraints.