# Suman Chakravorty

### From Dynamics & Control Research Group Wiki

(diff) ←Older revision | Current revision | Newer revision→ (diff)

THIS WEBPAGE IS OLD. PLEASE GO TO THE NEW WEBSITE: [1]

Howdy! You have reached the webpage of Suman Chakravorty, Associate Professor with the Department of Aerospace Engineering at Texas A&M University, College Station. I completed my B.Tech from the Indian Institute of Technology, Madras, in Mechanical Engineering, in 1997, and my PhD in Aerospace Engineering from the University of Michigan, Ann Arbor, in 2004. I was an Assistant Professor at A&M from August 2004 - September 2010, and have been in my current position since then.

My research focuses on ameliorating the triple curses of NONLINEARITY, DIMENSIONALITY and UNCERTAINTY in estimation and control problems, with applications to Mobile Robotics, Morphing Aircraft and Distributed Parameter Systems. In the following, you will find a brief description of my research interests, along with our publications, according to the field of interest. Thank you for showing interest in our research work and please feel free to contact us if you have questions regarding any of our research problems.

## Contents |

## Research Interests

THIS WEBPAGE IS OLD. PLEASE GO TO THE NEW WEBSITE: edplab.org

### Robotic Sensing and Planning

We pursue research on sensing of, and planning in large, spatially distributed uncertain environments, using distributed mobile sensor platforms. This research is at the confluence of control theory, robotics, AI and information theory. Imagine a mobile robot that has to build a map of an unknown area without the help of GPS, and it has to accomplish this in an optimal fashion, and in real time: this is the problem of Simultaneous Planning, Localization and Mapping (SPLAM). Solving the SPLAM problem might one day enable the autonomous operation of systems in disaster affected areas, planetary exploration, submarine exploration and homeland security among myriad other applications of great practical interest. As with any practical real-world Control problem, it involves an estimation-theoretic and a control theoretic problem. The estimation theoretic problem being one of estimating a model of the environment using only relative measurements while the planning problem is that of adaptively controling the robotic platform based on the current model of the environment so as to accomplish some objective. The problem is made very hard because of the extreme high dimensionality of the environment and hence, the estimation and control methods have to be both robust to uncertainties as well as computationally, i.e., the methods need to be implementable in real-time. We are developing robust and computationally efficient hybrid Bayesian Frequentist methods for solving the SLAM problem with guaranteed performance. We are also developing generalized sampling based feedback planners for the planning of high DOF robotic systems under process and sensing uncertainty, these methods being termed the Generalized Probabilistic Roadmap (GPRM) and the Feedback Aware Information Roadmap (FIRM) respectively.

### Stochastic Dynamical Systems and Nonlinear Filtering

In this work , we pursue the problem of uncertainty propagation in complex nonlinear dynamical systems, specifically through the design of robust computational methods for the solution of the Fokker-Planck-Kolmogorov Equation. The Fokker-Planck-Kolmogorov equation is at the core of any stochastic analysis and design problem. Specific applications of these methods are to the control of morphing wing aircraft and to the Air Traffic Control problem. We also pursue research on nonlinear filtering, in particular, we are very interested in the problem of space situational awareness and how advanced nonlinear filtering techniques based on the FPK equation can be applied to this problem. We are also very interested in the nonlinear filtering of very high dimensional systems, for instance, systems governed by PDEs as opposed to ODEs, that routinely have states in the order of millions. Such problems pose very unique and difficult challenges to conventional nonlinear filtering techniques such as the Kalman Filter. This work is at the intersection of this broad research thrust and that of robotic mapping and planning above.

### Space Based High Resolution Imaging Systems

In this field, we have researched the design of space based imaging systems that are capable of imaging earth and space-based objects at heretofore unheard of resolutions. Such systems combine the light from smaller telescopes that are space apart to form a synthetic aperture that is euivalent to a much larger monolithic aperture. Such systems have application in the field of space situational awareness, for the protection of space-based assets and in Science missions such as the detection and imaging of distant exo-solar planets and other such interesting distant astronomical phenomenon.

## Students

Expectations from prospective students.

I expect my students to have a solid background in Math and Physics (Mechanics). There is going to be a fair amount of Mathematics, especially applied probability, required for research in our field of interest. Finally, there is going to be a fair amount of computational work too in our research since at the core of our research, we are trying to solve very high dimensional estimation and control problems. Though not absolutely necessary, good skills in C/C++ and Matlab would be a great plus for this work.

If you have read through the website so far, I strongly encourage you to apply for a position in our group. Good Luck.

### PhD students

*Current:*

Aliakbar Aghamohammadi

Dan Yu

Saurav Agrawal

*Past:*

Mrinal Kumar (Dec. 2009, Assistant Professor at U. Florida, Gainesville)

Roshmik Saha (August 2011, Microsoft Corp.)

Sandip Kumar (December 2011, Mathworks)

### Masters Students

*Current:*

Kiran Sajjanshetty

*Past:*

Josh Davis (graduated 2006, now at Bell Helicopters, Dallas)

Jaime Ramirez (graduated 2006, PhD from MIT)

## Teaching

ENGR 111: Introduction to Engineering I ENGR 112: Introduction to Engineering II Aero 201: Introduction to Aerospace Engineering Aero 211: Introduction to Aerospace Engineering Mechanics Aero 310: Aerospace Dynamics Aero 422: Active Control of Aerospace Vehicles Aero 630: Introduction to Random Dynamical Systems

## Awards

Best Paper in conference award at the 2006 AAS Astrodynamics Specialist Conference, Breckenridge, CO

Several "best paper in session" awards at the American Control Conference (ACC)

Air Force Summer Faculty Fellow (2010, 2011)