2018 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the algorithmic foundations of motion planning in robotics and computational biology and leadership in broadening participation in computing.
2015 - ACM Fellow For contributions to robotics and leadership in broadening participation in computing.
2013 - Fellow of the American Association for the Advancement of Science (AAAS)
2012 - ACM Distinguished Member
2010 - IEEE Fellow For contributions to the algorithmic foundations of motion planning in robotics and computational biology
Her main research concerns Motion planning, Artificial intelligence, Probabilistic roadmap, Algorithm and Human–computer interaction. Her Motion planning research is within the category of Robot. Her studies deal with areas such as Machine learning, Computer animation and Computer vision as well as Artificial intelligence.
Nancy M. Amato has researched Probabilistic roadmap in several fields, including Workspace, Dynamical systems theory, Scalability and Linear-quadratic-Gaussian control. Her Algorithm research integrates issues from Process and Configuration space. Her research integrates issues of Visualization, Iterative method and Haptic technology in her study of Human–computer interaction.
Her scientific interests lie mostly in Motion planning, Artificial intelligence, Robot, Probabilistic roadmap and Parallel computing. Her study looks at the relationship between Motion planning and fields such as Mathematical optimization, as well as how they intersect with chemical problems. Her Artificial intelligence study combines topics in areas such as Machine learning and Computer vision.
Her Robot study integrates concerns from other disciplines, such as Control engineering, Control reconfiguration, Distributed computing and Topology. The various areas that Nancy M. Amato examines in her Probabilistic roadmap study include Graph, Theoretical computer science, Folding and Human–computer interaction. Her studies in Parallel computing integrate themes in fields like Programming language, Data structure and Scalability.
Nancy M. Amato focuses on Motion planning, Robot, Artificial intelligence, Distributed computing and Path. Nancy M. Amato interconnects Representation, Set, Task, Pathfinding and Workspace in the investigation of issues within Motion planning. Her biological study spans a wide range of topics, including Scheme and Motion.
Her work on Robotics, Probabilistic roadmap and Local learning as part of general Artificial intelligence study is frequently linked to Obstacle, therefore connecting diverse disciplines of science. Many of her studies on Probabilistic roadmap involve topics that are commonly interrelated, such as Folding. Her Distributed computing research incorporates elements of Scalability, Identification, Collision, Robot kinematics and Trajectory.
Nancy M. Amato mainly investigates Robot, Path, Motion planning, Theoretical computer science and Distributed computing. Her Robot study introduces a deeper knowledge of Artificial intelligence. Many of her research projects under Artificial intelligence are closely connected to Obstacle with Obstacle, tying the diverse disciplines of science together.
Nancy M. Amato focuses mostly in the field of Motion planning, narrowing it down to matters related to Workspace and, in some cases, Probabilistic logic and Bounding overwatch. The concepts of her Theoretical computer science study are interwoven with issues in Data flow diagram, Algorithmic skeleton, Synchronization, Skeleton and Probabilistic roadmap. Her work carried out in the field of Scheme brings together such families of science as Mathematical optimization and Mobile robot.
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OBPRM: an obstacle-based PRM for 3D workspaces
Nancy M. Amato;O. Burchan Bayazit;Lucia K. Dale;Christopher Jones.
workshop on the algorithmic foundations of robotics (1998)
A randomized roadmap method for path and manipulation planning
N.M. Amato;Y. Wu.
international conference on robotics and automation (1996)
MAPRM: a probabilistic roadmap planner with sampling on the medial axis of the free space
S.A. Wilmarth;N.M. Amato;P.F. Stiller.
international conference on robotics and automation (1999)
Choosing good distance metrics and local planners for probabilistic roadmap methods
N.M. Amato;O.B. Bayazit;L.K. Dale;C. Jones.
international conference on robotics and automation (1998)
Using motion planning to study protein folding pathways
Guang Song;Nancy M. Amato.
research in computational molecular biology (2001)
Using motion planning to study protein folding pathways.
Nancy M. Amato;Guang Song.
Journal of Computational Biology (2002)
FIRM: Sampling-based feedback motion-planning under motion uncertainty and imperfect measurements
Ali-Akbar Agha-Mohammadi;Suman Chakravorty;Nancy M Amato.
The International Journal of Robotics Research (2014)
An obstacle-based rapidly-exploring random tree
Rodriguez;Xinyu Tang;Jyh-Ming Lien;N.M. Amato.
international conference on robotics and automation (2006)
Approximate convex decomposition of polygons
Jyh-Ming Lien;Nancy M. Amato.
Computational Geometry: Theory and Applications (2006)
Multithreaded Asynchronous Graph Traversal for In-Memory and Semi-External Memory
Roger Pearce;Roger Pearce;Maya Gokhale;Nancy M. Amato.
ieee international conference on high performance computing data and analytics (2010)
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