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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.
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Proceedings Article
OBPRM: an obstacle-based PRM for 3D workspaces
TL;DR: This paper presents a new class of randomized path planning methods, known as Probabilistic Roadmap Methods (prms), which use randomization to construct a graph of representative paths in C-space whose vertices correspond to collision-free con gurations of the robot.
Proceedings ArticleDOI
A randomized roadmap method for path and manipulation planning
Nancy M. Amato,Y. Wu +1 more
TL;DR: A new randomized roadmap method for motion planning for many DOF robots that can be used to obtain high quality roadmaps even when C-space is crowded, with the main novelty in the authors' approach is that roadmap candidate points are chosen on C-obstacle surfaces.
Proceedings ArticleDOI
MAPRM: a probabilistic roadmap planner with sampling on the medial axis of the free space
TL;DR: A new method of sampling the configuration space in which randomly generated configurations are retracted onto the medial axis of the free space is proposed, and it is shown that sampling and retracting in this manner increases the number of nodes found in small volume corridors in a way that is independent of the volume of the corridor and depends only on the characteristics of the obstacles bounding it.
Journal ArticleDOI
FIRM: Sampling-based feedback motion-planning under motion uncertainty and imperfect measurements
TL;DR: FIRM is introduced as an abstract framework, a multi-query approach for planning under uncertainty which is a belief-space variant of probabilistic roadmap methods and the so-called SLQG-FIRM, a concrete instantiation of FIRM that focuses on kinematic systems and then extends to dynamical systems by sampling in the equilibrium space.
Proceedings ArticleDOI
Choosing good distance metrics and local planners for probabilistic roadmap methods
TL;DR: A new local planning method is proposed, called rotate-at-s, that outperforms the common straight-line in C-space method in crowded environments and includes recommendations for selecting appropriate combinations of distance metrics and local planners for use in motion planning methods, particularly probabilistic roadmap methods.