2020 - Member of Academia Europaea
2019 - ACM AAAI Allen Newell Award For pioneering contributions to robotic motion planning and their applications in bioinformatics and biomedicine, including the invention of randomized motion planning algorithms and probabilistic roadmaps.
2017 - ACM Athena Lecturer Award For the invention of randomized motion planning algorithms in robotics and the development of robotics-inspired methods for bioinformatics and biomedicine.
2012 - Fellow of the American Association for the Advancement of Science (AAAS)
2012 - IEEE Fellow For contributions to robot-motion planning and computational biology
2012 - Member of the National Academy of Medicine (NAM)
2010 - ACM Fellow For contributions to robotic motion planning and its application to computational biology.
2008 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the field of automated motion planning and the development of widely used probabilistic search algorithms
2004 - Fellow of the Indian National Academy of Engineering (INAE)
2000 - Fellow of Alfred P. Sloan Foundation
2000 - ACM Grace Murray Hopper Award For her seminal work on the probabilistic roadmap approach which has caused a paradigm shift in the area of path planning, and has many applications in robotics, manufacturing, nanotechnology and computational biology.
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 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.
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
L.E. Kavraki;P. Svestka;J.-C. Latombe;M.H. Overmars.
international conference on robotics and automation (1996)
The Open Motion Planning Library
I. A. Sucan;M. Moll;L. E. Kavraki.
IEEE Robotics & Automation Magazine (2012)
Path planning using lazy PRM
R. Bohlin;L.E. Kavraki.
international conference on robotics and automation (2000)
Practical robust localization over large-scale 802.11 wireless networks
Andreas Haeberlen;Eliot Flannery;Andrew M. Ladd;Algis Rudys.
acm/ieee international conference on mobile computing and networking (2004)
Robotics-based location sensing using wireless Ethernet
Andrew M. Ladd;Kostas E. Bekris;Algis Rudys;Lydia E. Kavraki.
Wireless Networks (2005)
Robotics-based location sensing using wireless ethernet
Andrew M. Ladd;Kostas E. Bekris;Algis Rudys;Guillaume Marceau.
acm/ieee international conference on mobile computing and networking (2002)
Randomized preprocessing of configuration for fast path planning
L. Kavraki;J.-C. Latombe.
international conference on robotics and automation (1994)
On finding narrow passages with probabilistic roadmap planners
David Hsu;Lydia E. Kavraki;Jean-Claude Latombe;Rajeev Motwani.
workshop on the algorithmic foundations of robotics (1998)
Analysis of probabilistic roadmaps for path planning
L.E. Kavraki;M.N. Kolountzakis;J.-C. Latombe.
international conference on robotics and automation (1996)
A random sampling scheme for path planning
Jér ocirc Barraquand;Lydia Kavraki;Jean-Claude Latombe;Rajeev Motwani.
international symposium on robotics (1997)
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