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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 Machine Learning Approach for Feature-Sensitive Motion Planning.
TL;DR: This paper uses a machine learning approach to characterize and partition C-space into regions that are well suited to one of the methods in the authors' library of roadmapbased motion planners, and demonstrates that the simple prototype system reliably outperforms any of the planners on their own.
Better group behaviors in complex environments using global roadmaps
TL;DR: In this article, the authors propose new techniques for four distinct group behaviors: homing, goal searching, traversing narrow areas and shepherding, which enable the creatures to modify their actions based on their current location and state.
Proceedings ArticleDOI
Parallel algorithms for higher-dimensional convex hulls
TL;DR: This work shows that the convex hull of n points in R/sup d/ can be constructed in O(log n) time using O(n log n+n/sup [d/2]/) work, with high probability, and how to make the randomized methods output-sensitive with only a small increase in running time.
Journal ArticleDOI
Distributed reconfiguration of metamorphic robot chains
TL;DR: This work describes distributed algorithms for reconfiguring a straight chain of hexagonal modules to any intersecting straight chain configuration and proves their algorithms are correct, and show that they are either optimal or asymptotically optimal in the number of moves and in the time required for parallel reconfiguration.
Proceedings ArticleDOI
Run-time methods for parallelizing partially parallel loops
TL;DR: A new run–time technique for finding an optimal parallel execution schedule for a partially parallel loop, i.e., a loop whose parallelization requires synchronization to ensure that the iterations are executed in the correct order.