Seminars, Minicourses & Lectures

 Session 4 | October 21, 2020
 
All Seminar Sessions Occur @ 12:15 - 1:15 (EST)
 
Panel on Applying to Faculty Positions in Robotics
GT Faculty Representing Various Colleges

Abstract: It is a vibrant time in robotics research in both academia and industry. In academia, a little strategy can go a long way in the faculty hiring process, especially in the face of a pandemic. In this panel discussion, we will discuss some of the in-and-outs of applying for (and ultimately getting) faculty positions in robotics.

 
 

IRIM's seminar series is video recorded and housed in Georgia Tech Library's SMARTech repository. Additionally, more than five years of historical information about the seminar series is available online.


Speaker 1 | Topic: Coverage and Inspection Planning for Unmanned Aerial Vehicles
Kevin Yu  | Virginia Polytechnic Institute and State University

Abstract: In this presentation, we investigate how to plan paths for Unmanned Aerial Vehicles (UAV) for coverage of an environment. I will present three increasingly complex coverage problems based on the environment. We start with a 2D point coverage problem where the UAV needs to visit a set of sites on the ground plane by flying at a fixed altitude above the ground. The UAV has limited battery capacity which may make it infeasible to visit all the points. We propose a novel symbiotic UAV and Unmanned Ground Vehicle (UGV) system where the UGV acts as a mobile recharging station. We present a practical, efficient algorithm for solving this problem using Generalized Traveling Salesperson Problem (GTSP) solver. We then extend this algorithm to covering 2D regions on the ground with UAVs that can operate in fixed-wing or multi-rotor modes. Finally, we propose to investigate a general version of the problem where the UAV is allowed to fly in full 3D space and the environment to be covered is in 3D as well. We propose an algorithm that clusters points in the free space to have a UAV autonomously plan online paths for bridge inspection. These online paths can be re-planned in real-time such that the UAV strives to obtain an optimal 3D coverage path.

Speaker 2 | Topic: Desensitization for Safe Planning under Parametric Uncertainties
Venkata Ramana Makkapati | Georgia Institute of Technology

Abstract: The tension between optimality and safety is often evident in robotics---particularly for applications that have stringent performance requirements---under conditions for which uncertainties in sensing, environment models, and control effectiveness are unavoidable. For all but the simplest applications, optimal solutions tend to bring the robot dangerously close to the operational safety margins. For example, it is well known that the shortest path for a mobile robot in a polygonal environment lies in the visibility graph which implies that the optimal path would contact the obstacles while traversing the path. While in practice it is typical to perturb paths slightly such that they do not reach the constraint boundaries, this safety strategy raises a number of significant questions: How should one perform these perturbations? How should one balance the cost of violating constraints against reduced performance? And, perhaps most importantly, how can one provide a principled evaluation of the effects of uncertainty with respect to the trade-offs between optimality and safety, and adjust the path to optimally balance between the two? It is this latter question that is addressed in the work.

The issue of safe optimal path planning under parametric uncertainties is addressed using a novel regularizer that allows trading off optimality with safety. The proposed regularizer leverages the notion that collisions may be modeled as constraint violations in an optimal control setting in order to produce open-loop trajectories with reduced risk of collisions. The risk of constraint violation is evaluated using a state-dependent relevance function and first-order variations in the constraint function with respect to parametric variations. The approach is generic and can be adapted to any optimal control formulation that deals with constraints under parametric uncertainty.


Visiting Faculty Fellows Mini-Courses

IRIM’s Visiting Faculty Fellows program supports extended visits (one to six months) to the Georgia Tech Atlanta campus by faculty members from other institutions or industry/government laboratories who are engaged in research activities focusing on robotics. IRIM provides Visiting Fellows with partial salary support, along with support for travel and living expenses. Visiting Fellows interact with IRIM faculty and students and teach a minicourse on their current research during their stay at Georgia Tech.

 IRIM Fellows Emeritus

2018

Nonlinear Control for Robots
Mark W. Spong - Professor of Systems Engineering, Professor of Electrical and Computer Engineering, and Excellence in Education Chair in the Erik Jonsson School of Engineering and Computer Science
The University of Texas at Dallas

Mark W. Spong received the Doctor of Science degree in systems science and mathematics in 1981 from Washington University in St. Louis. He has held faculty positions at Lehigh University, Cornell University, and at the University of Illinois at Urbana-Champaign. Currently, he is a professor of Systems Engineering, professor of Electrical and Computer Engineering and holder of the Excellence in Education Chair in the Erik Jonsson School of Engineering and Computer Science at the University of Texas at Dallas. He was Dean of the Jonsson School at UT Dallas from 2008-2017. During his tenure as dean he added four departments of engineering, nine new degree programs, and more than doubled the number of students and faculty.

Review Dynamics of Robot, Feedback Linearization, I/O Linearization and Zero Dynamics

Control of Underactuated Robots I

Control of Underactuated Robots II

Control of Underactuated Robots III, Control of Nonholonomic Systems I

Control of Nonholonomic Systems II

Control of Nonholonomic Systems III

2017

Stochastic Methods for Robotics
Gregory S. Chirikjian - Professor; Department of Mechanical Engineering, Johns Hopkins

Chirikjian’s research interests lie in robotics, automation and manufacturing; biomolecular mechanics, conformational analysis and nanoscience; mathematical crystallography; medical image registration, fiducial design and reconstruction; and in mathematical modeling and computational mathematics. He has developed numerical and analytical techniques for efficient computation of motion in binary robot arm design. He holds four patents for his work.

Lecture 1: Stochastic Methods for Robotics

Lecture 2: Stochastic Methods for Robotics

Lecture 3: Stochastic Methods for Robotics

Lecture 4: Stochastic Methods for Robotics

Lecture 5: Stochastic Methods for Robotics

Lecture 6: Stochastic Methods for Robotics

Lecture 7: Stochastic Methods for Robotics

Lecture 8: Stochastic Methods for Robotics


“Life as a Professor” Video Series

Affiliated Center Seminars

Two of IRIM's affiliated centers also host weekly seminars. The Machine Learning Center holds seminars on Wednesdays at 12:15 p.m., alternating weekly with IRIM's schedule. The Decision and Control Laboratory (DCL) typically holds seminars on Fridays at 11 a.m.