1. Lab for Autonomous & Intelligent Robotic Systems (LAIRS)
Puneet Singla
Assistant Professor
September 18, 2018
Department of Mechanical & Aerospace Engineering
The State University of New York, Buffalo
In this talk, my main interest is to present a novel nonlinear system identification technique to deal with the issues of nonlinearity and high dimensioned output vector in an efficient manner

2. Research Overview Intelligent & Autonomous Systems Research Areas
Uncertainty Propagation
Guidance & Navigation
Research Areas
Mesh-Less FEM
Input-Output Approximation
Adaptive & Distributed Control
My braod areas of interest lie in ….as part of my larger interest in Intelligent and Autonomous Systems and …
During my graduate studies at A&M, I have taken every opportunity to explore these areas by participating in various research projects.
Today, first I will share some of these exciting research activities followed by the details on my system identification algorithm.
Multi-Scale Approximation Methods

3. Automated Navigation & Guidance for UxV
Terrain Guided Automated Landing.
Safe Landing Site
UAV & UGV Collaboration to track Evaders.

4. Test Beds

5. Uncertainty Propagation: An Illustration
The Double Pendulum: A computer simulation using neighboring initial states

6. The Focus of Our Research: Potentially Hazardous Asteroids
2000PH5 in number of Earth –Radii
Miss-Distance between Earth and
,000
Will it hit?
When will it hit?
Image Copyrights:
Petr Scheirich
… a bustling neighborhood !
Time in Years
9/18/2018

7. Image Guided Radiation Therapy
Novel adaptive algorithms to precisely predict the motion of tumor & Internal Organs
Correlation of real-time imagery data from external & internal markers.
Here we have a baseline method from the literature (forgot the ref, will have to look at Puneet’s reference list) which can solve a large linear programming problem. Puneet’s developments use GLO_MAP functions to interpolate the control inputs near-continuously, using the coefficients of the basis functions as the “effective control inputs” This provides an order reduction. In the figures above, two possibilities are considered, the 22,500 actuator inputs were interpolated crudely with only 8 local approximations, or with 25 local approximations. Each local approximation could use up to six basis functions.
Notice, as time proceeds in the example, some regions cannot be approximated adequately with the 8 local approximations, however 25 local approximations are accurate over all time for this simulation.
The lower right figure show the solution time required for the full 22,500 unknowns versus the GLO-MAP solution with 25 local approximations, versus time along this particular motion. It is obvious that two orders of magnitude reduction in computational time is achieved with no loss of accuracy. These results suggest a basis for optimism that a real-time algorithm may be feasible for many distributed actuation problems.
Collaborators: Prof. Singh (MAE), Dr. Yang (Rosewell Park)

8. Thank You Q?
9/18/2018