Optimal Control Synthesis for Multi-Robot Systems under High-Level Task Specifications
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), that are developed in concurrency theory. Finding feasible robot paths that satisfy LTL-specified tasks can be achieved using tools from model checking theory. On the other hand, finding optimal robot paths that optimize a desired performance metric can be done using tools from optimal control synthesis. The goal of this project is to 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.
 Probabilistic Motion Planning under Temporal Tasks and Soft Constraints. M. Guo and M. M. Zavlanos. IEEE Transactions on Automatic Control, Vol. PP, No. 99, pp. 1-1, January 2018, DOI: 10.1109/TAC.2018.2799561.
 Sampling-Based Optimal Control Synthesis for Multi-Robot Systems under Global Temporal Tasks.Y. Kantaros and M. M. Zavlanos. IEEE Transactions on Automatic Control, Vol. PP, No. 99, pp. 1-1, July 2018, DOI: 10.1109/TAC.2018.2853558.
Distributed Optimization Algorithms for Networked Systems
Distributed optimization algorithms are iterative methods that allow to decompose an optimization problem into smaller, more manageable subproblems that are solved in parallel by a group of agents or processors. For this reason, they are widely used to solve large-scale problems arising in areas as diverse as wireless communications, optimal control, machine learning, artificial intelligence, computational biology, finance, and statistics, or problems with a separable structure that are amenable to distributed implementations. Moreover, distributed algorithms avoid the cost and fragility associated with centralized coordination, and provide better privacy for the autonomous decision makers. These are desirable properties, especially in applications involving networked robots, sensors, or wireless radios. The goal of this project is to develop new distributed optimization algorithms that converge fast so that they can be used for real-time control of robot networks, are computationally light so that they can be used on devices with computation and power limitations (such as wireless radios and sensors), are online so that they can be used to make adaptive decisions when data become available in real-time, and can handle uncertainty in the problem data.
 Approximate Projections for Decentralized Optimization with Functional Constraints. S. Lee and M. M. Zavlanos. IEEE Transactions on Automatic Control, Vol. PP, No. 99, pp. 1-1, November 2017, DOI: 10.1109/TAC.2017.2778696.
 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.
Intermittent Connectivity Control of Mobile Robot Networks (Support: ONR)
The goal of this project is to develop new distributed methods that enable intermittent communication in teams of mobile robots. Wireless communication is known to play a pivotal role in enabling teams of robots to successfully accomplish global coordinated tasks. For this reason, in recent years, there has been a large amount of work focused on designing controllers that ensure point-to-point or end-to-end network connectivity for all time. Nevertheless, 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). Moreover, maintaining all-time connectivity in these applications can severely restrict the robots from accomplishing their tasks, as motion planning is always constrained by network connectivity constraints. Instead, a much preferred solution is to enable robots to communicate in an intermittent fashion, and operate in disconnect mode the rest of the time. While in disconnect mode, the robots can accomplish other tasks free of communication constraints.
 Multi-Robot Data Gathering under Buffer Constraints and Intermittent Communication. M. Guo and M. M. Zavlanos. IEEE Transactions on Robotics, Vol. 34, No. 4, pp. 1082-1097, August 2018.
 Distributed Intermittent Connectivity Control of Mobile Robot Networks. Y. Kantaros and M. M. Zavlanos. IEEE Transactions on Automatic Control, Vol. 62, No. 7, pp. 3109-3121, July 2017.
Model-Based Source Identification using Mobile Robot Sensors
The goal of this project is to develop a new framework for model-based Source Identification (SI) using teams of mobile robot sensors. Many existing approaches to active SI 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 handle point sources and provide no information about their intensity, and they can not be used in non-convex domains easily. These limitations can be addressed using model-based SI techniques that are a special case of methods used in Inverse Problems (IPs). Model-based methods rely on a mathematical model of the underlying physical phenomenon that is usually a Partial Differential Equation (PDE) and boundary conditions, e.g., the advection-diffusion transport problem. Methods for model-based SI typically assume that a set of state measurements is available. The goal of this project is to develop model-based Active Source Identification (ASI) methods to allow teams of mobile robots to optimally collect measurements in real-time and solve real-world SI problems in complex environments under uncertainty.
 Model-Based Active Source Identification in Complex Environments. R. Khodayi-mehr, W. Aquino, and M. M. Zavlanos. IEEE Transactions on Robotics, under review.
 Model-Based Sparse Source Identification. R. Khodayi-mehr, W. Aquino, and M. M. Zavlanos. Proc. 2015 American Control Conference, Chicago, IL, July 2015, pp. 1818-1823.
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.
 Global Planning and Communication Control for Multi-Robot Networks in Complex Environments. Y. Kantaros and M. M. Zavlanos. IEEE Transactions on Robotics, Vol. 32, No. 5, pp. 1045-1061, October 2016.
 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.
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.
 Distributed Hierarchical Control for State Estimation with Robotic Sensor Networks. C. Freundlich, Y. Zhang, and M. M. Zavlanos. IEEE Transactions on Control of Network Systems, Vol. PP, No. 99, pp. 1-1, December 2017, DOI: 10.1109/TCNS.2017.2782481.
 Controlling a Robotic Stereo Camera under Image Quantization Noise. C. Freundlich, Y. Zhang, A. Zhu, P. Mordohai, and M. M. Zavlanos. International Journal of Robotics Research, Vol. 36, No. 12, pp. 1268-1285, October 2017.
Michael M. Zavlanos
Department of Mechanical Engineering & Materials Science
Michael M. Zavlanos | Last Update 01.02.2019
CURRENT PROJECTS | FORMER PROJECTS