8.L. Velazquez, G. Palardy, and C. Barbalata, “A robotic 3D printer for UV-curable thermosets: Dimensionality prediction using a data-driven approach,” International Journal of Computer Integrated Manufacturing, pp. 1–18, 2023.

7.H. O. Erkol, M. Bailey, G. Palardy, and C. Barbalata, “Predicting composite laminates roughness:Data-driven modeling approaches using force sensor data from robotic manipulators,” The International Journal of Advanced Manufacturing Technology, vol. 128, no. 3-4, pp. 1801–1813, 2023.

6.I. Carlucho, D. Stephens, W. Ard, and C. Barbalata, “Semi-parametric control architecture for autonomous underwater vehicles subject to time delays”, IEEE Access, vol. 11, pp. 71 287–71 300, 2023.

5.E. Morgan, I. Carlucho, W. Ard, and C. Barbalata, “Autonomous underwater manipulation: Current trends in dynamics, control, planning, perception, and future directions,” Current Robotics Reports, pp. 1–12, 2022.

4.I. Carlucho, D. Stephens, and C. Barbalata, “An adaptive data-driven controller for underwater manipulators with variable payload”, Applied Ocean Research, vol. 113, paper no. 102726, 2021.

3.C. Barbalata, M. W. Dunnigan, and Y. Petillot, “Coupled and decoupled force/motion controllers for an underwater vehicle-manipulator system”, Journal of Marine Science and Engineering, vol. 6, no. 3, article 96, 2018.

2.C. Barbalata, M. W. Dunnigan, and Y. Petillot, “Position/force operational space control for under-water manipulation”, Journal of Robotics and Autonomous Systems, vol. 100, pp. 150–159, 2018.

1.C. Barbalata and L. S. Mattos, “Laryngeal tumor detection and classification in endoscopic video”, IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 1, pp. 322–332, 2014.

17.E. Morgan, W. Ard, and C. Barbalata, “Model-based visual control for robotic manipulators using Udwadia Kalaba formulation,” in ASME International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, vol. 87639, 2023.

16.E. Morgan, W. Ard, and C. Barbalata, “A probabilistic framework for hydrodynamic parameter estimation for underwater manipulators,” in IEEE OCEANS 2023-MTS/IEEE US Gulf Coast, 2023, pp. 1–9.

15.J. Bardarson, J. Clement, S. Dahiya, M. R. Gartia, and C. Barbalata, “Modeling and control of a novel thermoelectric cooling system,” in ASME International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, vol. 86663, 2022.

14.J. Nguyen, M. Bailey, I. Carlucho, and C. Barbalata, “Robotic manipulators performing smart sanding operation: A vibration approach,” in IEEE International Conference on Robotics and Automation (ICRA), pp. 2958–2964, 2022.

13.J. Oubre, W. Ard, J. Nguyen, and C. Barbalata, “Towards a fully autonomous robotic system for detection and removal of surface defects in fiber glass panels,” in 6th IFAC International Conference on Intelligent Control and Automation Sciences, 2022.

12.L. Velazquez, G. Palardy, and C. Barbalata, “Design and integration of end-effector for 3D printing of novel uv-curable shape memory polymers with a collaborative robotic system,” in The Composites and Advance Materials Expo, 2021.

11.I. Carlucho, D. Stephens, and C. Barbalata, “Insights into a data driven optimal control for energy efficient manipulation”, Proc. Global OCEANS Conf. and Exp., Singapore, 2020.

10.I. Carlucho, M. de Paula, C. Barbalata, and G.G. Acosta, “A reinforcement learning control approach for underwater manipulation under position and torque constraints”, Proc. Global OCEANS Conf. and Exp., Singapore, 2020.

9.C. Barbalata, R. Vasudevan, and M. Johnson-Roberson, “A constrained control-planning strategy for redundant manipulators”, Proc. IEEE Intl. Conf. Robotics and Automation , pp. 3073–3079, 2019.

8.C. Barbalata, E. Olson, L. Chrobak, R. Camilli, and M. Johnson-Roberson, “From teleoperation to autonomy for hydraulic underwater manipulators”, in Astrobiology Science Conference, 2019.

7.A. Olson, C. Barbalata, J. Zhang, K. A. Skinner, and M. Johnson-Roberson, “Synthetic data generation for deep learning of underwater disparity estimation”, in MTS/IEEE OCEANS Charleston , 2018.

6.E. Iscar, C. Barbalata, N. Goumas, and M. Johnson-Roberson, “Towards low cost, deep water AUV optical mapping”, in MTS/IEEE OCEANS Charleston , 2018.

5.C. Barbalata, E. Iscar, and M. Johnson-Roberson, “Experimental evaluation of depth controllers for a small-size AUV”, in IEEE/OES Autonomous Underwater Vehicle Workshop , 2018.

4.L. Bezanson, S. Reed, E. M. Martin, J. Vasquez, and C. Barbalata, “Coupled control of a lightweight ROV and manipulator arm for intervention tasks”, in IEEE OCEANS Anchorage , 2017.

3.C. Barbalata, M. W. Dunnigan, and Y. Petillot, “Reduction of the dynamic coupling in an underwater vehicle-manipulator system using an inverse dynamic model approach”, Proc. IFAC Workshop Navigation, Guidance and Control of Underwater Vehicles , pp. 44–49, 2015.

2.E. Tusa, A. Akbarinia, R. G. Rodriguez, and C. Barbalata, “Real-time face detection and tracking utilising open-mp and ROS”, Proc. IEEE Asia-Pacific Confe. Computer Aided System Engineering , pp. 179–184, 2015.

1.C. Barbalata, V. De Carolis, M. W. Dunnigan, Y. R. Petillot, and D. M. Lane, “An adaptive controller for autonomous underwater vehicles”, Proc. IEEE Intl. Conf. Intelligent Robots and Systems , pp. 1658–1663, 2015.