What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Control theory
His primary areas of study are Mobile robot, Control theory, Kinematics, Robot and Artificial intelligence.
The Mobile robot study combines topics in areas such as Control engineering and Motion planning.
His study of Trajectory is a part of Control theory.
His Kinematics research integrates issues from Mechanism, Robotics, Robot locomotion and Inverse problem.
J.W. Burdick has included themes like Stochastic programming, Dynamic programming, Mathematical optimization and Differential equation in his Robot study.
J.W. Burdick has researched Artificial intelligence in several fields, including Algorithm and Computer vision.
His most cited work include:
- A modal approach to hyper-redundant manipulator kinematics (400 citations)
- The kinematics of hyper-redundant robot locomotion (324 citations)
- On the inverse kinematics of redundant manipulators: characterization of the self-motion manifolds (213 citations)
What are the main themes of his work throughout his whole career to date?
J.W. Burdick mostly deals with Control theory, Mobile robot, Robot, Artificial intelligence and Kinematics.
His studies in Control theory integrate themes in fields like Control engineering and Motion control.
J.W. Burdick studied Mobile robot and Motion planning that intersect with Computational geometry and Mathematical optimization.
His Robot locomotion and Obstacle avoidance study in the realm of Robot interacts with subjects such as Terrain.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Algorithm, Machine learning and Computer vision.
His study in the field of Inverse kinematics is also linked to topics like Redundancy.
He most often published in these fields:
- Control theory (42.68%)
- Mobile robot (29.27%)
- Robot (29.27%)
What were the highlights of his more recent work (between 2003-2012)?
- Artificial intelligence (25.61%)
- Mobile robot (29.27%)
- Computer vision (15.85%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Mobile robot, Computer vision, Robot and Motion planning.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Search algorithm.
His research integrates issues of Computational geometry, Filter, Feature and Extended Kalman filter in his study of Mobile robot.
His Robot research is multidisciplinary, incorporating elements of Representation, Group, Boundary and Operations research.
His Motion planning study incorporates themes from Control engineering, Distributed computing and Mathematical optimization.
As a part of the same scientific study, he usually deals with the Control engineering, concentrating on Motion control and frequently concerns with Mechanical system, Kinematics, Control theory and Dynamical system.
Between 2003 and 2012, his most popular works were:
- Robot Motion Planning in Dynamic, Uncertain Environments (180 citations)
- On a decentralized active sensing strategy using mobile sensor platforms in a network (133 citations)
- Probabilistic Collision Checking With Chance Constraints (80 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Mathematical analysis
- Algorithm
J.W. Burdick spends much of his time researching Mobile robot, Mathematical optimization, Distributed computing, Control engineering and Motion planning.
The Simultaneous localization and mapping research J.W. Burdick does as part of his general Mobile robot study is frequently linked to other disciplines of science, such as Stochastic process, therefore creating a link between diverse domains of science.
His studies deal with areas such as Probabilistic logic, Obstacle avoidance and Approximation theory as well as Mathematical optimization.
His study in Distributed computing intersects with areas of studies such as SIMPLE, Mobile wireless sensor network, Brooks–Iyengar algorithm, Task and Wireless sensor network.
The concepts of his Control engineering study are interwoven with issues in Robot motion planning and Motion control.
His Motion planning research is under the purview of Robot.
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