IRIM Seminar Series

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Now showing 1 - 10 of 34
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    Towards Visual Route Following for Mobile Robots…Forever!
    (Georgia Institute of Technology, 2013-03-20) Barfoot, Tim ; Georgia Institute of Technology. Center for Robotics and Intelligent Machines ; University of Toronto. Institute for Aerospace Studies
    In this talk I will describe a particular approach to visual route following for mobile robots that we have developed, called Visual Teach & Repeat (VT&R), and what I think the next steps are to make this system usable in real-world applications. We can think of VT&R as a simple form of simultaneous localization and mapping (without the loop closures) along with a path-tracking controller; the idea is to pilot a robot manually along a route once and then be able to repeat the route (in its own tracks) autonomously many, many times using only visual feedback. VT&R is useful for such applications as load delivery (mining), sample return (space exploration), and perimeter patrol (security). Despite having demonstrated this technique for over 300 km of driving on several different robots, there are still many challenges we must meet before we can say this technique is ready for real-world applications. These include (i) visual scene changes such as lighting, (ii) physical scene changes such as path obstructions, and (iii) vehicle changes such as tire wear. I’ll discuss our progress to date in addressing these issues and the next steps moving forward. There will be lots of videos.
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    Robust Control Tools for Validating UAS Flight Controllers
    (Georgia Institute of Technology, 2022-11-16) Farhood, Mazen ; Georgia Institute of Technology. Institute for Robotics and Intelligent Machines ; Virginia Polytechnic Institute and State University. Kevin T. Crofton Department of Aerospace and Ocean Engineering
    This talk presents a framework based on robust control theory to aid in the certification process of unmanned aircraft system (UAS) flight controllers. Uncertainties are characterized and quantified based on mathematical models and flight test data obtained in-house for a small, commercial, off-the-shelf platform with a custom autopilot. These uncertainties are incorporated via a linear fractional transformation to model the uncertain UAS. Utilizing integral quadratic constraint (IQC) theory to assess the uncertain UAS worst-case performance, it is demonstrated that this framework can determine system sensitivities to uncertainties, compare the robustness of controllers, tune controllers, and indicate when controllers are not sufficiently robust. To ensure repeatability, this framework is used to tune, compare, and analyze a suite of controllers, including path-following, trajectory-tracking, H-infinity, H2, and PID controllers. By employing a non-deterministic simulation environment and conducting numerous flight tests, it is shown that the uncertain UAS framework reliably predicts loss of control, compares the robustness of different controllers, and provides tuned controllers which are sufficiently robust. Furthermore, robust performance guarantees from IQC analysis can be used to provide worst-case bounds on the UAS state at each point in time, providing an inexpensive and robust mathematical tool to aid in the certification of UAS flight controllers.
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    Reinforcement Learning: Leveraging Deep Learning for Control
    (Georgia Institute of Technology, 2020-11-04) Buhr, Craig ; Georgia Institute of Technology. Institute for Robotics and Intelligent Machines ; MathWorks
    Reinforcement learning is getting a lot of attention lately. People are excited about its potential to solve complex problems in areas such as robotics and automated driving, where traditional control methods can be challenging to use. In addition to deep neural nets to represent the policy, reinforcement learning lends itself to control problems because its training incorporates repeated exploration of the environment. As such exploration is time-consuming and costly or dangerous when done with actual hardware, a simulation model is often used to represent the environment. In this talk, we provide an overview of reinforcement learning and its application to teaching a robot to walk. We discuss the differences between reinforcement learning and traditional control methods. Specific topics of reinforcement learning covered in this presentation include: • Creating environment models • Crafting effective reward functions • Deploying to embedded devices through automatic code generation for CPUs and GPUs
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    Interactive Sports Analytics: Going Beyond Spreadsheets
    ( 2016-03-30) Lucey, Patrick ; Georgia Institute of Technology. Institute for Robotics and Intelligent Machine ; STATS LLC
    Imagine watching a sports game live and having the ability to find all plays which are similar to what just happened immediately. Better still, imagine having the ability to draw a play with the x’s and o’s on an interface, like a coach draws up on a chalkboard and finding all the plays like that instantaneously and conduct analytics on those plays (i.e., when those plays occur, how many points a team expects from that play). Additionally, imagine having the ability to evaluate the performance of a player in a given situation and compare it against another player in exactly the same position. We call this approach “Interactive Sports Analytics” and in this talk, I will describe methods to find play similarity using multi-agent trajectory data, as well as predicting fine-grain plays. I will show examples using STATS SportVU data in basketball, Prozone data in soccer and Hawk-Eye in tennis.
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    Mechanical Intelligence in Robotic Manipulation: Towards Human-Level Dexterity in Robotic and Prosthetic Hands
    (Georgia Institute of Technology, 2018-10-10) Dollar, Aaron M. ; Georgia Institute of Technology. Institute for Robotics and Intelligent Machines ; Yale University
    The human hand is the pinnacle of dexterity – it has the ability to powerfully grasp a wide range of object sizes and shapes as well as delicately manipulate objects held within the fingertips. Current robotic and prosthetic systems, however, have only a fraction of that manual dexterity. My group attempts to address this gap in three main ways: examining the mechanics and design of effective hands, studying biological hand function as inspiration and performance benchmarking, and developing novel control approaches that accommodate task uncertainty. In terms of hand design, we strongly prioritize passive mechanics, including incorporating adaptive underactuated transmissions and carefully tuned compliance, and seek to maximize open-loop performance while minimizing complexity. To motivate and benchmark our efforts, we are examining human hand usage during daily activities as well as quantifying functional aspects such as precision manipulation workspaces.
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    Magnetic Capsule Robots for Gastrointestinal Endoscopy and Abdominal Surgery
    (Georgia Institute of Technology, 2013-09-04) Valdastri, Pietro ; Vanderbilt University
    The talk will move from capsule robots for gastrointestinal endoscopy toward a new generation of surgical robots and devices, having a relevant reduction in invasiveness as the main driver for innovation. Wireless capsule endoscopy has already been extremely helpful for the diagnosis of diseases in the small intestine. Specific wireless capsule endoscopes have been proposed for colon inspection, but have never reached the diagnostic accuracy of standard colonoscopy. In the first part of the talk, we will discuss enabling technologies that have the potential to transform colonoscopy into a painless procedure. These technologies include magnetic manipulation of capsule endoscopes, real-time pose tracking, and intermagnetic force measurement. The second part of the talk will provide an overview about the development of novel robotic solutions for single incision robotic surgery. In particular, a novel surgical robotic platform based on local magnetic actuation will be presented as a possible approach to further minimize access trauma. The final part of the talk will introduce the novel concept of intraoperative wireless tissue palpation, presenting a capsule that can be directly manipulated by the surgeon to create a stiffness distribution map in real-time. This stiffness map can then be used to guide tissue resection with the goal of minimizing the healthy tissue being removed with the tumor.
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    Multirobot Coordination: From High-level Specification to Correct Execution
    ( 2015-12-02) Ayanian, Nora ; Georgia Institute of Technology. Institute for Robotics and Intelligent Machine ; University of Southern California. Computer Science Dept.
    Using a group of robots in place of a single complex robot to accomplish a task has many benefits, including simplified system repair, less down time, and lower cost. Combining heterogeneous groups of these multi-robot systems allows addressing multiple subtasks in parallel, reducing the time it takes to address many problems, such as search and rescue, reconnaissance, and mine detection. These missions demand different roles for robots, necessitating a strategy for coordinated autonomy while respecting any constraints the environment may impose. Synthesis of control policies for heterogeneous multirobot systems is particularly challenging because of inter-robot constraints such as communication maintenance and collision avoidance, the need to coordinate robots within groups, and the dynamics of individual robots. I will present approaches to synthesizing feedback policies for navigating groups of robots in constrained environments. These approaches automatically and concurrently solve both the path planning and control synthesis problems, and are specified at a high level, for example, using an iPad interface to navigate a complex environment with a team of UAVs. I will also present some preliminary work on novel approaches to developing controllers for many types of multirobot tasks, by using crowdsourced multi-player game data.
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    Cyber Human Interaction: A Control Systems/Robotics Perspective on Functional Electrical Stimulation
    (Georgia Institute of Technology, 2017-10-04) Dixon, Warren ; Georgia Institute of Technology. Institute for Robotics and Intelligent Machines ; University of Florida
    Application of an electric field across skeletal muscle causes muscle contractions that produce limb movement. Clinicians have long prescribed electrical stimulation as a means to strengthen muscle; however, clinicians have had a growing interest in electrical stimulation to evoke coordinated limb motions for functional tasks such as cycling. Motivation for such a cybernetic system includes advanced rehabilitative outcomes (i.e., neuroplasticity and restoration of function) for individuals with neurological disorders. A challenge to developing these outcomes is that muscle activation dynamics are uncertain and nonlinear, and the dynamics of limb motion also require the coordinated switching among multiple muscle groups. Moreover, artificial stimulation of the muscle is highly inefficient, leading to rapid muscle fatigue, which can limit the therapeutic outcomes. This talk focuses on how perspectives from and advances in robotics/automation/control systems can be used to overcome these challenges. Underlying theories and experimental results for various closed-loop electrical stimulation methods will be described, including recent advances in cybernetic cycling where a robotic bicycle is combined with an electrically stimulated person to facilitate various rehabilitative objectives.
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    Challenges and Opportunities within Maritime Autonomy and the Naval Surface Warfare Center, Panama City Division
    ( 2016-09-08) Bays, Matthew ; Georgia Institute of Technology. Institute for Robotics and Intelligent Machine ; Naval Surface Warfare Center, Panama City Division
    Interest in unmanned systems has increased considerably within the maritime domain and specifically the U.S. Navy over the last several decades. However, the littoral (shallow water) and undersea environments offer unique challenges resulting in the need for more autonomous, more reliable, and more modular unmanned systems than is often found in other domains. In this talk we will provide an overview of the particular challenges the U.S. Navy is attempting to solve within the littoral environment and solutions currently in development. We will provide a brief overview of the Naval Surface Warfare Center, Panama City Division and then discuss select projects related to our key autonomy thrust areas of payload autonomy, architecture design, behavior/algorithm development, and test/evaluation of autonomous systems to meet these challenges. Finally, we will provide an overview of multiple U.S. Navy and DoD opportunities for collaboration.
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    Robust Agility and Safety for Dynamic Aerial Manipulation and Legged Locomotion
    (Georgia Institute of Technology, 2016-10) Sreenath, Koushil ; Georgia Institute of Technology. Institute for Robotics and Intelligent Machines ; Carnegie-Mellon University
    Biological systems are able to move with great elegance, agility, and efficiency in a wide range of environments. Endowing machines with similar capabilities requires designing controllers that can address the challenges of high-degree-of-freedom, high-degree-of-underactuation, nonlinear dynamics, while simultaneously enforcing constraints of available actuators, sensors and processors. In this talk, I will present the design of planning and control policies for two problems - dynamic aerial manipulation and dynamic legged locomotion. First, I will show how a coordinate-free, geometric mechanics formulation of the dynamics of a quadrotor carrying a suspended payload allows us to synthesize nonlinear geometric controllers with almost-global stability properties for aggressive maneuvers. I will present the problem of cooperative transportation of a cable-suspended payload using multiple aerial robots, and show how we can design dynamically feasible trajectories that can handle hybrid dynamics resulting from the cable tension going to zero. Next, I will present the design of control policies for dynamic bipedal locomotion by explicitly considering the nonlinear and hybrid dynamics of bipedal robots subject to input torque constraints, contact force constraints, and safety-critical constraints. This is achieved through control Lyapunov and Barrier functions. In addition, I will show that the adverse of effects of model uncertainty on both stability and constraint enforcement can be addressed through a robust formulation of control Lyapunov and Barrier functions.