Corina Barbalata
Associate Professor Louisiana State University
- Baton Rouge LA
Dr. Barbalata and her group are working on proposing solutions for real-world robotics applications.
Biography
Areas of Expertise
Research Focus
Marine Robotics and Autonomous Systems
Dr. Barbalata is proposing solutions for real-world robotics applications with social and environmental merits, through the combination of theoretical, computational, and experimental methods for the design new reactive capabilities for autonomous robotic systems working in complex and dynamic environments.
Education
Heriot-Watt University
Ph.D.
2017
Media Appearances
Chance Maritime Technologies and LSU Join Forces on Uncrewed Underwater Research
Ocean News online
2024-11-14
Led by LSU Mechanical Engineering Assistant Professor Corina Barbalata and her students—Donovan Gegg, Mikhalib Green, and Edward Morgan—the project is a collaboration between the LSU College of Engineering and Chance Maritime Technologies, headquartered in Lafayette. The two parties were brought together by Integer Technologies, which is involved in another of Barbalata’s current research projects.
Research team uses robots to search for shipwrecks
The Alpena News online
2022-06-06
“We have two remotely-operated vehicles, Dory and Nemo,” said Corina Barbalata of Louisiana State University in the Department of Mechanical and Industrial Engineering. “So, with Dory, what we are trying to do is making autonomous by adding some sensors, but still keeping it low-cost.”
LSU Engineering Faculty Design Sensor to Improve Vision of Underwater Robots
Science X online
2022-03-31
Barbalata, an assistant professor in the LSU Department of Mechanical & Industrial Engineering, is helping with the testing of the imaging system that will be deployed in an underwater vehicle and used to help the vehicle navigate the environment.
"We are going to look at tracking various aspects in the underwater environment and design a control structure based on this computational imaging system," she said.
Articles
On the Stabilization of Directed Formation Using Geometric Algebra Approach
IEEE Control Systems Letters2025
Distance-based formation control on minimally rigid graphs often encounters ambiguous shapes, where agents form incorrect shape formations due to multiple equilibrium points in the error dynamics. Recent studies on distance-based controllers, even those that introduce extra control variables, struggle to resolve this fundamental issue fully, typically requiring specific conditions or failing to manage reflections effectively. In this paper, we address both flip and reflection ambiguities in formation control by reexamining core geometric constraints and integrating them with traditional bearing-based formation control methods. We propose a novel controller that guarantees convergence to the correct formation without imposing any additional conditions. Numerical simulations demonstrate the controller’s effectiveness in avoiding formation ambiguities.
RecGS: Removing Water Caustic with Recurrent Gaussian Splatting
RecGS: Removing Water Caustic with Recurrent Gaussian Splatting2024
Water caustics are commonly observed in seafloor imaging data from shallow-water areas. Traditional methods that remove caustic patterns from images often rely on 2D filtering or pre-training on an annotated dataset, hindering the performance when generalizing to real-world seafloor data with 3D structures. In this letter, we present a novel method Recurrent Gaussian Splatting (RecGS), which takes advantage of today's photorealistic 3D reconstruction technology, 3D Gaussian Splatting (3DGS), to separate caustics from seafloor imagery. With a sequence of images taken by an underwater robot, we build 3DGS recurrently and decompose the caustic with low-pass filtering in each iteration. In the experiments, we analyze and compare with different methods, including joint optimization, 2D filtering, and deep learning approaches.
Dynamic event-triggered control of linear continuous-time systems using a positive systems approach
Nonlinear Analysis: Hybrid Systems2024
We provide new dynamic event-triggered controls for continuous-time linear systems that contain additive uncertainties. We prove input-to-state stability properties that imply uniform global exponential stability when the additive uncertainties are zero. Significant novel features include (a) new dynamic extensions and new trigger rules that provide a new positive systems analog of significant prior dynamic event-triggered work of A. Girard and (b) our application to a BlueROV2 underwater vehicle model, where we provide significantly larger lower bounds on the inter-execution times, and usefully fewer trigger times, compared with standard dynamic event-triggered approaches that used the usual Euclidean norm, and as compared with static event-triggered controls that instead used positive systems approaches.
A robotic 3D printer for UV-curable thermosets: dimensionality prediction using a data-driven approach
International Journal of Computer Integrated Manufacturing2024
This paper presents a robotic 3D printer specifically designed for ultraviolet (UV)-curable thermosets, whose printing parameters can be selected by using a predictive modeling strategy. A specialized extruder head was designed and integrated with a UR5e robotic arm. Software packages were developed to enable the communication between the extruder head and the robotic arm, and control systems were implemented to regulate the printing process. A predictive approach using either a feedforward neural network (FNN) or convolution neural network (CNN) is proposed for estimating the dimensions of future prints based on the process parameters. This enables selection of the appropriate parameters for high-quality prints. This strategy aims to decrease expensive trial-and-error campaigns for material and printing parameter tuning.
Water wave optimization algorithm-based dynamic optimal dispatch considering a day-ahead load forecasting in a microgrid
IEEE Access2024
A novel strategy is proposed to tackle an optimal dispatch of a microgrid in response to dynamic conditions, utilizing a water wave optimization (WWO) algorithm and considering a day-ahead load forecasting. Amongst meta-heuristic algorithms, the WWO algorithm stands out in terms of population size, parameter tuning, exploitation and exploration, convergence speed, as well as optimization mechanism. It leverages its ability to efficiently explore solution spaces and adapt to changing conditions. It is applied to the dynamic optimal dispatch of a microgrid with the uncertainty of load power considered and solved by day-ahead load forecasting. It dynamically adjusts the microgrid operation in response to these inputs, ensuring optimal decision-making in the face of varying load scenarios.
Affiliations
- Marine Technology Society (MTS)
- Institute of Electrical and Electronics Engineers (IEEE)
- Coastal Studies Institute, Louisiana State University : Fellow
Media
