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Nancy M. Amato
Researcher at University of Illinois at Urbana–Champaign
Publications - 273
Citations - 9552
Nancy M. Amato is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Motion planning & Probabilistic roadmap. The author has an hindex of 51, co-authored 268 publications receiving 8988 citations. Previous affiliations of Nancy M. Amato include Texas A&M University & Google.
Papers
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A motion planning approach to protein folding
Guang Song,Nancy M. Amato +1 more
TL;DR: The novel motion planning based approach is the first simulation method that enables the study of protein folding kinetics at a level of detail that is appropriate (i.e., not too detailed or too coarse) for capturing possible 2-state and 3-state folding Kinetics that may coexist in one protein.
Book ChapterDOI
The STAPL pView
Antal Buss,Adam Fidel,Harshvardhan,Timmie Smith,Gabriel Tanase,Nathan Thomas,Xiabing Xu,Mauro Bianco,Nancy M. Amato,Lawrence Rauchwerger +9 more
TL;DR: The stapl pView concept and its properties are described, which generalize the iterator concept and enable parallelism by providing random access to, and an ADT for, collections of elements.
Proceedings ArticleDOI
Distributed reconfiguration of hexagonal metamorphic robots in two dimensions
TL;DR: In this article, a distributed algorithm for reconfiguring a straight chain into an admissible goal configuration is presented, and different heuristics are proposed to improve the performance of the reconfiguration algorithm.
Proceedings ArticleDOI
On the probabilistic completeness of the sampling-based feedback motion planners in belief space
TL;DR: It is shown that under mild conditions the sampling-based methods constructed based on the abstract framework of FIRM (Feedback-based Information Roadmap Method) are probabilistically complete under uncertainty.
Proceedings ArticleDOI
Sampling based motion planning with reachable volumes: Application to manipulators and closed chain systems
TL;DR: It is shown that reachableVolume samples are less likely to be invalid due to self-collisions, making reachable volume sampling significantly more efficient for higher dimensional problems, and are easier to connect than others, resulting in better connected roadmaps.