Reactive Learning in Underwater Exploration
This project focuses on automating exploration for underwater vehicles that operate in highly uncertain environments, including both environmental uncertainty and internal dynamic uncertainty. Environmental uncertainty can arise because the world may not be easy to model and agents in the world may behave in unexpected ways. Internal uncertainty can arise because the mechanics of motion are uncertain, for instance due to unknown and unknowable fluid mechanics. The work involves developing online algorithms for underwater vehicles in Xiaobo Tan's group at Michigan State University, a collaborator on this project. Moreover, decentralized/distributed algorithms are being developed as part of the work.
People
Giorgos Mamakoukas (Ph.D. Student)
Collaborators
Xiaobo Tan, Michigan State University
Publications
Local Koopman operators for data-driven control of robotic systems
G. Mamakoukas, M. Castano, X. Tan, and T. D. Murphey
Robotics: Science and Systems Proceedings, 2019. PDF
Feedback synthesis for underactuated systems using sequential second-order needle variations
G. Mamakoukas, M. Maciver, and T. D. Murphey
International Journal of Robotics Research, vol. 37, no. 13-14, pp. 1826–1853, 2019. PDF
Feedback synthesis for controllable underactuated systems using sequential second order actions
G. Mamakoukas, M. MacIver, and T. D. Murphey
Robotics: Science and Systems Proceedings, 2017. PDF, Video
Ergodic exploration of distributed information
L. Miller, Y. Silverman, M. A. MacIver, and T. D. Murphey
IEEE Transactions on Robotics, vol. 32, no. 1, pp. 36–52, 2016. PDF
Controlling simulated underactuated underwater vehicles with added mass and velocity drift using sequential action control
G. Mamakoukas, M. MacIver, and T. D. Murphey
American Controls Conf. (ACC), pp. 4500 – 4506, 2016. PDF
Funding
This project is funded by the National Science Foundation–Information and Intelligent Systems: Information-driven Autonomous Exploration in Uncertain Underwater Environments.
Other Projects
Active Learning and Data-Driven Control
Active Perception in Human-Swarm Collaboration
Algorithmic Matter and Emergent Computation
Control for Nonlinear and Hybrid Systems
Cyber Physical Systems in Uncertain Environments
Harmonious Navigation in Human Crowds
Information Maximizing Clinical Diagnostics
Reactive Learning in Underwater Exploration
Robot-Assisted Rehabilitation
Software-Enabled Biomedical Devices