Interactive & Emergent Autonomy Lab

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 Information Maximizing Clinical Diagnostics Reactive Learning in Underwater Exploration Robot-Assisted Rehabilitation Software-Enabled Biomedical Devices

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.