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Optimal Control Synthesis for High-Level Robot Tasks


The basic motion planning problem consists of generating robot trajectories that reach a given goal region from an initial configuration while avoiding obstacles. More recently, a new class of planning approaches have been developed that can handle a richer class of tasks, than the classical point-to-point navigation, and can capture temporal and boolean requirements. Such tasks can be, e.g., sequencing or coverage, data gathering, intermittent communication, or persistent surveillance, and can be captured using formal languages, such as Linear Temporal Logic (LTL). In this research, we develop optimal control synthesis methods that scale to large numbers of robots and can handle known or unknown uncertainty in the workspace properties, the robot actions, and the task outcomes. Research supported by AFOSR and ONR.

Selected Publications:


X. Luo and M. M. Zavlanos, "Temporal Logic Task Allocation in Heterogeneous Multi-Robot Systems," IEEE Transactions on Robotics, vol. 38, no. 6, pp. 3602 - 3621, Dec. 2022.


Y. Kantaros and M. M. Zavlanos, "STyLuS*: A Temporal Logic Optimal Control Synthesis Algorithm for Large-Scale Multi-Robot Systems," International Journal of Robotics Research, vol. 39, no. 7, pp. 812-836, Jun. 2020.

Intermittent Connectivity Control of Robot Networks


Due to the uncertainty in the wireless channel, that affects signal strength in an unpredictable way, it is often impossible to ensure all-time connectivity in practice. This is more so the case in underwater environments that are severely communications limited (short range, noisy, low BW). In this research, we develop new distributed methods that enable intermittent communication in teams of mobile robots. While in disconnect mode, the robots can accomplish other tasks free of communication constraints. Research supported by ONR.

Selected Publications:

M. Guo and M. M. Zavlanos, "Multi-Robot Data Gathering under Buffer Constraints and Intermittent Communication," IEEE Transactions on Robotics, vol. 34, no. 4, pp. 1082-1097, Aug. 2018.

Y. Kantaros and M. M. Zavlanos, "Distributed Intermittent Connectivity Control of Mobile Robot Networks," IEEE Transactions on Automatic Control, vol. 62, no. 7, pp. 3109-3121, Jul. 2017.

Physics-Based Learning for Robotic Environmental Sensing


Many existing approaches to robotic environmental sensing are heuristics that drive the robot upwind or in the concentration ascent direction, so that it stays in the plume. These methods are sometimes successful in practice, but they are also sensor-specific, they can only track point sources, they provide no information about their intensity, and they can not be used in complex environments easily. In this research, we develop model-based methods for environmental sensing that rely on mathematical models of the underlying physical phenomenon to control teams of mobile robots to optimally collect measurements and track sources in complex environments under uncertainty. Research supported by NSF.

Selected Publications:

R. Khodayi-mehr, P. Jian, and M. M. Zavlanos, "Physics-Guided Active Learning of Environmental Flow Fields," Proc. 5th Conference on Learning for Dynamics and Control (L4DC), ser. Proc. of Machine Learning Research (PMLR), N. Matni, M. Morari, and G. J. Pappas, Eds., vol. 211, pp. 928-940, Jun. 2023.


R. Khodayi-mehr, W. Aquino, and M. M. Zavlanos, "Model-Based Active Source Identification in Complex Environments," IEEE Transactions on Robotics, vol. 35, no. 3, pp. 633-652, Jun. 2019.

Smart Ultrasound Imaging


Ultrasound is widely accepted as one of the best forms of medical imaging compared to similar technologies, such as CT scans and MRI, due its low operating cost and safety for the patients. At the same time, it is well known that there can be large variability in image quality obtained by different experts imaging the same patient. In this research, we develop a new active ultrasound system where expert users interact with a smart ultrasound device in order to improve medical imaging and facilitate diagnosis. Research supported by NSF.

Selected Publications:


R. Khodayi-mehr, M. W. Urban, M. M. Zavlanos, and W. Aquino, "Plane Wave Elastography: A Frequency-Domain Ultrasound Shear Wave Elastography Approach," Physics in Medicine & Biology, vol. 66, no. 12, pp. 125017, Jun. 2021.

Optimization of Smart Manufacturing Systems


In this research, we design an agile manufacturing exchange system (MES) in which suppliers of raw materials, assemblers, transportation companies, etc., participate through standardized protocols to fulfill complex manufacturing orders. This design provides the foundation for a smart software mediation layer (i.e., a "broker") that enables 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, we develop a distributed real-time optimization and knowledge discovery framework that addresses workflow optimization, resource allocation, and data-driven performance prediction in a dynamic and uncertain manufacturing network of users, brokers, and providers. Research supported by NSF.

Selected Publications:

S. Lee, A. Ribeiro, and M. M. Zavlanos, "Distributed Continuous-time Online Optimization using Saddle-Point Methods," Proc. 55th IEEE Conference on Decision and Control (CDC), Las Vegas, NV, Dec. 2016, pp. 4314-4319.

S. Lee and M. M. Zavlanos, "Distributed Primal-Dual Methods for Online Constrained Optimization," Proc. 2016 American Control Conference (ACC), Boston, MA, Jul. 2016, pp. 7171-7176.

Control of Mobile Microrobot Teams


External electromagnetically powered microrobots require the same control signal to be sent to all the microrobots in the workspace. While control strategies for such robots exist, 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 this research, we develop a new framework to individually control teams of mobile mictorobots using external electromagnetic fields. Research supported by NSF.

Selected Publications:

Y. Kantaros, B. Johnson, S. Chowdhury, D. J. Cappelleri, and M. M. Zavlanos, "Control of Magnetic Microrobot Teams for Temporal Micromanipulation Tasks," IEEE Transactions on Robotics, vol. 34, no. 6, pp. 1472-1489, Dec. 2018.

D. Cappelleri, D. Efthymiou, A. Goswami, N. Vitoroulis, and M. M. Zavlanos, "Towards Mobile Microrobot Swarms for Additive Micromanufacturing," International Journal of Advanced Robotic Systems, vol. 11, no. 150, pp. 1-14, Sep. 2014.

Control of Wireless Robot Networks


Wireless communication is known to play a pivotal role in enabling teams of mobile robots to successfully accomplish global coordinated tasks. While graphs are frequently used to model inter-robot communications in these systems, it has long been recognized that since links in a wireless network do not entail tangible connections, associating links with arcs on a graph can be somewhat arbitrary. In this research, we employ practical communication models, that rely on routing and rate control as well as beamforming, to model inter-robot communications and develop distributed methods to control the interplay between the physical space of robot motion and the space of wireless communications. We also develop methods to control wireless networks so that information sharing and coordination between robots is improved. Research supported by NSF and ONR.

Selected Publications:

N. Chatzipanagiotis, Y. Liu, A. P. Petropulu, and M. M. Zavlanos, "Distributed Cooperative Beamforming in Multi-Source Multi-Destination Clustered Systems," IEEE Transactions on Signal Processing, vol. 62, no. 23, pp. 6105-6117, Dec. 2014.

M. M. Zavlanos, A. Ribeiro, and G. J. Pappas, "Network Integrity in Mobile Robotic Networks," IEEE Transactions on Automatic Control, vol. 58, no. 1, pp. 3-18, Jan. 2013.

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. In this research, we develop distributed and provable algorithms for connectivity control of robot networks with dynamically changing communication topologies.

Selected Publications:

M. M. Zavlanos, M. B. Egerstedt, and G. J. Pappas, "Graph Theoretic Connectivity Control of Mobile Robot Networks," Proceedings of the IEEE: Special Issue on Swarming in Natural and Engineered Systems, vol. 99, no. 9, pp. 1525-1540, Sep. 2011.

M. M. Zavlanos and G. J. Pappas, "Distributed Connectivity Control of Mobile Networks," IEEE Transactions on Robotics, vol. 24, no. 6, pp. 1416-1428, Dec. 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 to this problem decouple the assignment problem from the motion planning problem that follows. While these approaches can be effective in solving static assignment problems, they fail to address the complexity of dynamic problems where the number of targets and agents change with time and the system need to adapt to these changes. In this research, we develop distributed algorithms for concurrent task assignment and motion planning in teams of mobile robots.

Selected Publications:

M. M. Zavlanos, L. Spesivtsev, and G. J. Pappas, "A Distributed Auction Algorithm for the Assignment Problem," Proc. 47th IEEE Conference on Decision and Control (CDC), Cancun, Mexico, Dec. 2008, pp. 1212-1217.

M. M. Zavlanos and G. J. Pappas, "Dynamic Assignment in Distributed Motion Planning with Local Coordination," IEEE Transactions on Robotics, vol. 24, no. 1, pp. 232-242, Feb. 2008.

Identification of Gene Regulatory Networks


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. In this research, we develop novel optimization techniques to identify genetic networks that explain data obtained from noisy genetic perturbation experiments or time course measurements.

Selected Publications:

M. M. Zavlanos, A. A. Julius, S. P. Boyd, and G. J. Pappas, "Inferring Stable Genetic Networks from Steady-State Data," Automatica, Special Issue on Systems Biology, vol. 47, no. 6, pp. 1113-1122, Jun. 2011.

A. A. Julius, M. M. Zavlanos, S. P. Boyd, and G. J. Pappas, "Genetic Network Identification using Convex Programming," IET Systems Biology, vol. 3, no. 3, pp. 155-166, May 2009.

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