Control, Modeling, and Perception of Autonomous Systems (CoMPAS) Laboratory seeks to build scalable marine robotics foundations — from creating the basic tools to enabling the main research lines for future developments. We aim to enable exploration without previous knowledge of the environment (such as seafloor maps) in complex terrain with multiple platforms. Based on addressing the fundamental technology challenges will allow getting closer to achieve persistence presence, ocean visualization, adaptive targeted sampling, environmental change detection, and repeated monitoring of the ocean, from benthic to midwater.

The major efforts expected over the next three years in marine robotics include:

  • Scalable Navigation: Enabling Simultaneous localization and mapping (SLAM) approach for known and unknown environments based on visual and acoustic information.
  • Scalable Control: Enabling advanced control in complex environments.
  • Scalable Platforms: Enabling multi-vehicle solutions with homogeneous/heterogeneous platforms.

Those are key elements for the autonomy of marine robotics platforms to enable multi-vehicle operations.

Publications

Rodríguez-Martínez, S., and G. Troni. 2025. Full Magnetometer and Gyroscope Bias Estimation using Angular Rates: Theory and Experimental Evaluation of a Factor Graph-Based Approach. IEEE Journal of Oceanic Engineering, 1–10. https://doi.org/10.1109/JOE.2024.3523701. Learn more.

Caress, D.W., E. Martin, M. Risi, G. Troni, A. Hamilton, C. Kecy, J. Paduan, H. Thomas, S. Rock, M. Wolfson-Schwehr, R. Henthorn, B. Hobson, and L. Bird. 2025. The MBARI Low Altitude Survey System for 1-cm-Scale Seafloor Surveys in the Deep Ocean. IEEE Journal of Oceanic Engineering, 1–12. https://doi.org/10.1109/JOE.2024.3521256

Rodríguez-Martínez, S., and G. Troni. 2024. Towards a Factor Graph-Based Method using Angular Rates for Full Magnetometer Calibration and Gyroscope Bias Estimation, In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Abu Dhabi, United Arab Emirates, 2024, 1199–1205. https://doi.org/10.1109/IROS58592.2024.10801438.

Xie, Y., G. Troni, N. Bore, and J. Folkesson. 2024. Bathymetric surveying with imaging sonar using neural volume rendering. IEEE Robotics and Automation Letters, 9(9): 8146–8153. https://doi.org/10.1109/LRA.2024.3440843

Muñoz, B. and G. Troni. 2024. Learning the Ego-Motion of an Underwater Imaging Sonar: A Comparative Experimental Evaluation of Novel CNN and RCNN Approaches. IEEE Robotics and Automation Letters, 9(3): 2072-2079. doi: 10.1109/LRA.2024.3352357