What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Algorithm
- Gene
Lydia E. Kavraki mostly deals with Motion planning, Robot, Artificial intelligence, Mathematical optimization and Probabilistic roadmap.
Lydia E. Kavraki combines subjects such as Workspace, Mobile robot and Configuration space with her study of Motion planning.
Her Robot research integrates issues from Algorithm, Kinematics, Graph and Distributed computing.
Her research integrates issues of Human–computer interaction and Computer vision in her study of Artificial intelligence.
Lydia E. Kavraki has included themes like Field, State space, Kinodynamic planning, Differential equation and Upper and lower bounds in her Mathematical optimization study.
The various areas that Lydia E. Kavraki examines in her Probabilistic roadmap study include Probabilistic automaton, Measure, Unification, Transitive closure and Operations research.
Her most cited work include:
- Probabilistic roadmaps for path planning in high-dimensional configuration spaces (4087 citations)
- The Open Motion Planning Library (866 citations)
- Practical robust localization over large-scale 802.11 wireless networks (636 citations)
What are the main themes of her work throughout her whole career to date?
Her primary scientific interests are in Motion planning, Artificial intelligence, Robot, Mathematical optimization and Robotics.
Her Motion planning research incorporates elements of Workspace, Motion and Mobile robot.
Her research in Artificial intelligence intersects with topics in Computer vision, Configuration space, Algorithm, Machine learning and Pattern recognition.
Her study on Robot kinematics is often connected to Abstraction as part of broader study in Robot.
Lydia E. Kavraki has researched Mathematical optimization in several fields, including Any-angle path planning, Probabilistic logic, Markov decision process and State space.
The study incorporates disciplines such as Field and Human–computer interaction in addition to Robotics.
She most often published in these fields:
- Motion planning (38.82%)
- Artificial intelligence (28.26%)
- Robot (23.60%)
What were the highlights of her more recent work (between 2015-2021)?
- Motion planning (38.82%)
- Artificial intelligence (28.26%)
- Robot (23.60%)
In recent papers she was focusing on the following fields of study:
Lydia E. Kavraki focuses on Motion planning, Artificial intelligence, Robot, Robotics and Computational biology.
Her Motion planning study is concerned with Path in general.
Her study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Scalability.
Her work deals with themes such as Mathematical optimization and Human–computer interaction, which intersect with Robot.
Her Robotics research is multidisciplinary, relying on both Engineering ethics, Data science and Big data.
Her Computational biology study integrates concerns from other disciplines, such as Docking, Cellular immunity, Cancer immunotherapy, Major histocompatibility complex and Peptide.
Between 2015 and 2021, her most popular works were:
- Incremental Task and Motion Planning: A Constraint-Based Approach (96 citations)
- Randomized Physics-Based Motion Planning for Grasping in Cluttered and Uncertain Environments (50 citations)
- Sampling-Based Methods for Motion Planning with Constraints (50 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Gene
Her primary areas of investigation include Motion planning, Robot, Artificial intelligence, Distributed computing and Task analysis.
Her Motion planning study deals with the bigger picture of Path.
Her study in Robot is interdisciplinary in nature, drawing from both Leverage and Temporal logic.
A large part of her Artificial intelligence studies is devoted to Robotics.
Degrees of freedom is closely connected to Humanoid robot in her research, which is encompassed under the umbrella topic of Robotics.
Her work in Distributed computing addresses subjects such as Workspace, which are connected to disciplines such as Kinematics.
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