no code implementations • 15 Mar 2024 • Zili Wang, Sean B. Andersson, Roberto Tron
Deep learning methods have been widely used in robotic applications, making learning-enabled control design for complex nonlinear systems a promising direction.
no code implementations • 18 Feb 2024 • Yancheng Zhu, Sean B. Andersson
This paper considers the problem of localizing a set of nodes in a wireless sensor network when both their positions and the parameters of the communication model are unknown.
no code implementations • 21 Aug 2023 • Guoyao Shen, Yancheng Zhu, Mengyu Li, Ryan McNaughton, Hernan Jara, Sean B. Andersson, Chad W. Farris, Stephan Anderson, Xin Zhang
Recent advances in MRI reconstruction have achieved remarkable success with deep learning-based models.
no code implementations • 22 Mar 2022 • Zili Wang, Sean B. Andersson, Roberto Tron
We form and solve a nonlinear optimization problem with the sum of path lengths of the agent trajectories as the objective and subject to the original equilibria and global convergence conditions for formation control.
no code implementations • 17 Jan 2022 • Samuel C. Pinto, Shirantha Welikala, Sean B. Andersson, Julien M. Hendrickx, Christos G. Cassandras
For a given visiting sequence, we prove that in an optimal dwelling time allocation the peak uncertainty is the same among all the targets.
no code implementations • 1 Apr 2021 • Samuel C. Pinto, Sean B. Andersson, Julien M. Hendrickx, Christos G. Cassandras
We investigate the problem of persistent monitoring, where a mobile agent has to survey multiple targets in an environment in order to estimate their internal states.