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.
Design and Distributed Optimization of Smart Manufacturing Networks (Support: NSF ECCS)
The goal of this project is to design an agile manufacturing exchange system (MES) in which suppliers of raw materials, assemblers, transportation companies, etc., will participate through standardized protocols to fulfill complex manufacturing orders. This design will provide the foundation for a smart software mediation layer (i.e., a "broker") that will enable a MES to be self-learning and adaptive to dynamic/diverse service requests and resource availability, as well as support a large network of service providers and users within a complex information ecosystem. To realize the proposed system, a distributed real-time optimization and knowledge discovery framework needs to be developed that will address workflow optimization, resource allocation, and data-driven performance prediction in a dynamic and uncertain manufacturing network of users, brokers, and providers. This level of adaptation, seamless efficiency, and uninterrupted service will constitute a significant step forward towards a smart MES.
 Distributed Primal-Dual Methods for Online Constrained Optimization. S. Lee and M. M. Zavlanos. Proc. 2016 American Control Conference, Boston, MA, July 2016, pp. 7171-7176.
The goal of this project is to develop a new distributed framework to let mobile wireless robots move as dictated by their assigned tasks, while ensuring reliable communications as necessary for the accomplishment of a mission. In the proposed framework, network connectivity is not defined based only on point-to-point proximity relations and graph theory, but on metrics that capture the complexity of the wireless channel and are of interest to the performance of the end-to-end communication between nodes or between nodes and a fixed infrastructure. Maintaining these communication capabilities introduces a tight interplay between the physical space of robot positions and velocities and the space of wireless communications. This project addresses this interplay and develops mobile communication networks that reconfigure and adapt to the mission in order to provide users with reliable and up-to-the-minute communications and availability of information in uncertain non-line-of-sight environments.
 An Augmented Lagrangian Method for Distributed Optimization. N. Chatzipanagiotis, D. Dentcheva, and M. M. Zavlanos. Mathematical Programming, Vol. 152, No. 1-2, pp. 405-434, August 2015.
 Network Integrity in Mobile Robotic Networks. M. M. Zavlanos, A. Ribeiro, and G. J. Pappas. IEEE Transactions on Automatic Control, Vol. 58, No. 1, pp. 3-18, January 2013.
Optimal Communication for Fast Sensor Network Coordination (Support: NSF NeTS)
The goal of this project is to develop a new framework to control the structure of wireless robot networks so that the performance of networked dynamical processes carried out by the robots, such as decentralized estimation, information spreading, or synchronization, is improved. As the behavior of such processes directly depends on the network eigenvalue spectra, new metrics need to be designed that relate the network spectra to the network structure and capture the complexity of the wireless channel and the richness of the communication space, e.g., the end-to-end rates and routing decisions. This project develops efficient approximations of these metrics that can be computed in a decentralized way, as well as ways to control them in the joint space of robot positions and network configurations. It points to a new direction in the design of networked dynamical processes where the network structure is not only controlled for connectivity, but also for optimization of the dynamical process itself.
 Distributed Network Design for Laplacian Eigenvalue Placement. V. M. Preciado and M. M. Zavlanos. IEEE Transactions on Control of Network Systems, March 2016. (DOI: 10.1109/TCNS.2016.2544249)
 Spectral Control of Mobile Robot Networks. M. M. Zavlanos, V. M. Preciado, and A. Jadbabaie. Proc. 2011 American Control Conference, San Francisco, CA, June 2011, pp. 3245-3250.
Controlling Teams of Autonomous Mobile Beamformers (Support: NSF NeTS)
The goal of this project is to develop a new framework to control teams of mobile robots, cooperating in a beamforming fashion, to transmit information between multiple source-destination pairs, while meeting quality-of-service constraints and consuming minimum power. This optimization depends on the wireless channel, but also on the robot positions, which offer an additional degree of freedom due to robot mobility. It gives rise to a novel system of mobile beamformers that allows for significant performance gains compared to static systems that do not consider mobility. Additionally, unlike other communication protocols, such as multi-hop, it does not require high node density which results in packet collisions and delays, unreliable links, and difficulties in routing. The approach proposed in this project ensures robust communications and longevity in challenging environments, arising during the transmission of high-rate data, such as video or images, or in environments where there is no line-of-sight.
 Distributed Cooperative Beamforming in Multi-Source Multi-Destination Clustered Systems. N. Chatzipanagiotis, Y. Liu, A. P. Petropulu, and M. M. Zavlanos. IEEE Transactions on Signal Processing, Vol. 62, No. 23, pp. 6105-6117, December 2014.
 Controlling Groups of Mobile Beamformers. N. Chatzipanagiotis, Y. Liu, A. P. Petropulu, and M. M. Zavlanos. Proc. 51st IEEE Conference on Decision and Control, Maui, Hawaii, December 2012, pp. 1984-1989.
Controlling Teams of Mobile Microrobots using External Electromagnetic Fields (Support: NSF RI)
The goal of this project is to develop a new framework to individually control teams of mobile mictorobots using external electromagnetic fields. External electromagnetically powered microrobots require the same control signal to be sent to all the microrobots in the workspace. Control strategies for this situation exist, but the tasks that can be achieved by multiple agents are limited by constraints such as a low number of robots, non-smooth trajectories, the robots meeting in the same location, congregating close together, and collision avoidance. In some cases, the control strategies assume microrobot capabilities that are not yet realizable in practice. The key idea that enables this work is to decompose the workspace into cells, each one defined by the convex hull of a set of active microcoils, and develop hierarchical control strategies to drive teams of microrobots, jointly, through sequences of cells until they reach their final destinations. This research will allow for truly independent and coordinated control of mobile magnetic microrobots.
 Towards Mobile Microrobot Swarms for Additive Micromanufacturing. D. Cappelleri, D. Efthymiou, A. Goswami, N. Vitoroulis, and M. M. Zavlanos. International Journal of Advanced Robotic Systems, Vol. 11, No. 150, pp. 1-14, September 2014.
Distributed Estimation and Control using Mobile Robot Networks (Support: NSF IGERT)
The goal of this project is to develop the theoretical foundations that will enable robotic sensor networks to autonomously and reliably explore and build maps of unknown and uncertain environments. These systems can be used for a wide range of tasks including environmental monitoring and mapping, infrastructure inspection, and search and rescue missions. While such tasks have been tested with great success in controlled lab environments, the sensing and coordination mechanisms needed for precise distributed localization, ego-motion estimation, and control in uncertain and unpredictable environments remain underdeveloped. The key idea that motivates this research is the explicit modeling of sensing uncertainty integrated with novel distributed mechanisms to cooperatively regulate it in the joint space of robot mobility and resource utilization. The result is a distributed network of mobile robotic sensors that allows for significant performance gains compared to systems that do not jointly optimize sensing, communication, and control.
 Hybrid Control for Mobile Target Localization with Stereo Vision. C. Freundlich, P. Mordohai, and M. M. Zavlanos. Proc. 52nd IEEE Conference on Decision and Control, Firenze, Italy, December 2013, pp. 2635-2640.
 A Hybrid Control Approach to the Next-Best-View Problem using Stereo Vision. C. Freundlich, P. Mordohai, and M. M. Zavlanos. Proc. 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, May 2013, pp. 4478-4483.
Michael M. Zavlanos
Department of Mechanical Engneering & Materials Science
Michael M. Zavlanos | Last Update 07.14.2017
CURRENT PROJECTS | FORMER PROJECTS