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Learning Environmental Models with Multi-Robot Teams Using a Dynamical Systems Approach / Tahiya Salam.
- Format:
- Book
- Thesis/Dissertation
- Author/Creator:
- Salam, Tahiya, author.
- Language:
- English
- Subjects (All):
- Robotics.
- Electrical and Systems Engineering--Penn dissertations.
- Penn dissertations--Electrical and Systems Engineering.
- Local Subjects:
- Robotics.
- Electrical and Systems Engineering--Penn dissertations.
- Penn dissertations--Electrical and Systems Engineering.
- Physical Description:
- 1 online resource (166 pages)
- Distribution:
- Ann Arbor : ProQuest Dissertations & Theses, 2022
- Contained In:
- Dissertations Abstracts International 84-03B.
- Place of Publication:
- [Philadelphia, Pennsylvania] : University of Pennsylvania, 2022.
- Language Note:
- English
- Summary:
- Robots monitoring complex, spatiotemporal phenomena require rich, meaningful representations of the environment. This thesis presents methods for representing the environment as a dynamical system with machine learning techniques. Specifically, we formulate machine learning methods that lend to data-driven modeling of the phenomena. The data-driven modeling explicitly leverages theoretical foundations of dynamical systems theory. Dynamical systems theory offers mathematical and physically interpretable intuitions about the environmental representation. The contributions presented include distributed algorithms, online adaptation, uncertainty quantification, and feature extraction to allow for the actualization of these techniques on-board robots. The environmental representations guide robot behavior in developing strategies such as optimal sensing and energy-efficient navigation. The methods and procedures provided in this thesis were verified across complex, spatiotemporal environments and on experimental robots.
- Notes:
- Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
- Advisors: Hsieh, M. Ani; Committee members: Chaudhari, Pratik; Pappas, George; Ramos, Fabio.
- Department: Electrical and Systems Engineering.
- Ph.D. University of Pennsylvania 2022.
- Local Notes:
- School code: 0175
- ISBN:
- 9798351434377
- Access Restriction:
- Restricted for use by site license.
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