What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Algorithm
- Programming language
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 most cited work include:
- OBPRM: an obstacle-based PRM for 3D workspaces (411 citations)
- A randomized roadmap method for path and manipulation planning (302 citations)
- MAPRM: a probabilistic roadmap planner with sampling on the medial axis of the free space (290 citations)
What are the main themes of her work throughout her whole career to date?
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.
She most often published in these fields:
- Motion planning (44.06%)
- Artificial intelligence (29.02%)
- Robot (21.68%)
What were the highlights of her more recent work (between 2014-2021)?
- Motion planning (44.06%)
- Robot (21.68%)
- Artificial intelligence (29.02%)
In recent papers she was focusing on the following fields of study:
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.
Between 2014 and 2021, her most popular works were:
- Simultaneous Localization and Planning for Physical Mobile Robots via Enabling Dynamic Replanning in Belief Space (21 citations)
- SLAP: Simultaneous Localization and Planning Under Uncertainty via Dynamic Replanning in Belief Space (17 citations)
- Improved roadmap connection via local learning for sampling based planners (14 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Algorithm
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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