World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
12478
World Ranking
3461
National Ranking
1669

Research.com Recognitions

  • 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

Overview

Nancy M. Amato is affiliated with the University of Illinois at Urbana-Champaign in the United States. Their research spans multiple fields with a primary focus on Computer Science and Engineering. Within these domains, their work extensively covers subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Aerospace Engineering, Control and Systems Engineering, and Mechanical Engineering.

The main topics addressed in Amato's research include Robotic Path Planning Algorithms, Robotics and Sensor-Based Localization, Modular Robots and Swarm Intelligence, AI-based Problem Solving and Planning, Computational Geometry and Mesh Generation, Robotic Mechanisms and Dynamics, and Algorithms and Data Compression.

They have a significant publication record with many papers appearing in respected venues. Frequent publication outlets include arXiv (Cornell University) with 24 publications, IEEE Robotics and Automation Letters and IEEE Transactions on Automation Science and Engineering with 10 publications each, IEEE Robotics & Automation Magazine with 3 publications, and Foundations and Trends in Robotics with 1 publication.

Recent notable papers are:

  • A Roadmap for US Robotics - From Internet to Robotics 2020 Edition (2021, Foundations and Trends in Robotics)
  • Multi-Robot Task and Motion Planning With Subtask Dependencies (2020, IEEE Robotics and Automation Letters)
  • Representation-Optimal Multi-Robot Motion Planning Using Conflict-Based Search (2021, IEEE Robotics and Automation Letters)
  • Provably optimal parallel transport sweeps on semi-structured grids (2020, Journal of Computational Physics)
  • Parallel Hierarchical Composition Conflict-Based Search for Optimal Multi-Agent Pathfinding (2021, IEEE Robotics and Automation Letters)

Amato collaborates frequently with several researchers, including Marco Morales, James Motes, Seth Hutchinson, Venkat Krovi, and Diane Uwacu.

The scientist has received multiple awards recognizing their contributions to robotics and computing leadership. Honors include:

  • Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), 2018, for contributions to the algorithmic foundations of motion planning in robotics and computational biology and leadership in broadening participation in computing
  • ACM Fellow, 2015, for work in robotics and leadership in computing participation
  • Fellow of the American Association for the Advancement of Science (AAAS), 2013
  • ACM Distinguished Member, 2012
  • IEEE Fellow, 2010, for contributions to algorithmic foundations of motion planning in robotics and computational biology

Best Publications

  • OBPRM: an obstacle-based PRM for 3D workspaces

    Nancy M. Amato;O. Burchan Bayazit;Lucia K. Dale;Christopher Jones

  • A randomized roadmap method for path and manipulation planning

    N.M. Amato;Y. Wu

  • MAPRM: a probabilistic roadmap planner with sampling on the medial axis of the free space

    S.A. Wilmarth;N.M. Amato;P.F. Stiller

  • Approximate convex decomposition of polygons

    Jyh-Ming Lien;Nancy M. Amato

  • Choosing good distance metrics and local planners for probabilistic roadmap methods

    N.M. Amato;O.B. Bayazit;L.K. Dale;C. Jones

  • FIRM: Sampling-based feedback motion-planning under motion uncertainty and imperfect measurements

    Ali-Akbar Agha-Mohammadi;Suman Chakravorty;Nancy M Amato

  • An obstacle-based rapidly-exploring random tree

    Rodriguez;Xinyu Tang;Jyh-Ming Lien;N.M. Amato

  • Using motion planning to study protein folding pathways

    Guang Song;Nancy M. Amato

  • Using motion planning to study protein folding pathways.

    Nancy M. Amato;Guang Song

  • Approximate convex decomposition of polyhedra

    Jyh-Ming Lien;Nancy M. Amato

  • Multithreaded Asynchronous Graph Traversal for In-Memory and Semi-External Memory

    Roger Pearce;Roger Pearce;Maya Gokhale;Nancy M. Amato

  • STAPL: an adaptive, generic parallel C++ library

    Ping An;Alin Jula;Silvius Rus;Steven Saunders

  • Using motion planning to map protein folding landscapes and analyze folding kinetics of known native structures.

    Nancy M. Amato;Ken A. Dill;Guang Song

  • Shepherding behaviors

    Jyh-Ming Lien;O.B. Bayazit;R.T. Sowell;S. Rodriguez

  • A framework for adaptive algorithm selection in STAPL

    Nathan Thomas;Gabriel Tanase;Olga Tkachyshyn;Jack Perdue

  • Simultaneous shape decomposition and skeletonization

    Jyh-Ming Lien;John Keyser;Nancy M. Amato

  • Shepherding Behaviors with Multiple Shepherds

    Jyh-Ming Lien;S. Rodriguez;J. Malric;N.M. Amato

  • Distributed reconfiguration of metamorphic robot chains

    Jennifer E. Walter;Jennifer L. Welch;Nancy M. Amato

  • A motion-planning approach to folding: from paper craft to protein folding

    Guang Song;N.M. Amato

  • Probabilistic roadmap methods are embarrassingly parallel

    N.M. Amato;L.K. Dale

Frequent Co-Authors

Lawrence Rauchwerger
Lawrence Rauchwerger University of Illinois at Urbana-Champaign
Michael T. Goodrich
Michael T. Goodrich University of California, Irvine
Martin Schulz
Martin Schulz Technical University of Munich
Jennifer L. Welch
Jennifer L. Welch Texas A&M University
Bronis R. de Supinski
Bronis R. de Supinski Lawrence Livermore National Laboratory
Franco P. Preparata
Franco P. Preparata Brown University
Bud Mishra
Bud Mishra New York University
Maya Gokhale
Maya Gokhale Lawrence Livermore National Laboratory
Robert D. Falgout
Robert D. Falgout Lawrence Livermore National Laboratory
Peter Brown
Peter Brown University of Oxford

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