What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Mechanical engineering
His primary areas of study are Control theory, Kinematics, Control engineering, Artificial intelligence and Torque.
His biological study focuses on Manipulator.
His biological study spans a wide range of topics, including Vertical plane, Estimation theory, Robot calibration and Acceleration.
His Control engineering research is multidisciplinary, relying on both Robot, Control, Robot manipulator and Actuator.
His work on Robotics as part of general Artificial intelligence research is frequently linked to Surface deformation, bridging the gap between disciplines.
His Torque study which covers Trajectory that intersects with Simulation, Control system, Coordinate system, Revolute joint and Haptic technology.
His most cited work include:
- Kinematic features of unrestrained vertical arm movements (829 citations)
- Dynamic interactions between limb segments during planar arm movement (748 citations)
- A Recursive Lagrangian Formulation of Maniputator Dynamics and a Comparative Study of Dynamics Formulation Complexity (745 citations)
What are the main themes of his work throughout his whole career to date?
John M. Hollerbach mainly focuses on Control theory, Artificial intelligence, Kinematics, Robot and Computer vision.
John M. Hollerbach has included themes like Control engineering and Simulation in his Control theory study.
His study in Robotics and Haptic technology falls within the category of Artificial intelligence.
His research in Kinematics intersects with topics in Joint, Estimation theory and Acceleration.
The concepts of his Computer vision study are interwoven with issues in Normal force and Shear force.
The various areas that John M. Hollerbach examines in his Torque study include Manipulator, Trajectory and Dynamics.
He most often published in these fields:
- Control theory (42.42%)
- Artificial intelligence (26.77%)
- Kinematics (19.70%)
What were the highlights of his more recent work (between 2006-2021)?
- Artificial intelligence (26.77%)
- Computer vision (17.17%)
- Simulation (15.66%)
In recent papers he was focusing on the following fields of study:
John M. Hollerbach focuses on Artificial intelligence, Computer vision, Simulation, Haptic technology and Control theory.
His work on Linear discriminant analysis and Principal component regression as part of general Artificial intelligence study is frequently connected to Finger force, Force sensor and Mean squared prediction error, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
John M. Hollerbach has researched Computer vision in several fields, including Normal force, Shear force and Calibration.
His studies deal with areas such as Control system, System model, Interface, System testing and Wind tunnel as well as Simulation.
John M. Hollerbach usually deals with Interface and limits it to topics linked to Treadmill and Kinematics and Motion measurement.
His studies in Control theory integrate themes in fields like Control engineering and Position.
Between 2006 and 2021, his most popular works were:
- Observability index selection for robot calibration (103 citations)
- Active robot calibration algorithm (31 citations)
- Estimation of Fingertip Force Direction With Computer Vision (28 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Mechanical engineering
- Electrical engineering
John M. Hollerbach mostly deals with Computer vision, Artificial intelligence, Image registration, Linear discriminant analysis and Robot calibration.
His Computer vision research is multidisciplinary, incorporating perspectives in Transducer and Calibration.
His research brings together the fields of Normal force and Artificial intelligence.
His Linear discriminant analysis study combines topics in areas such as Image resolution, Robot, Track and Touchpad.
His biological study deals with issues like Observability, which deal with fields such as Calibration, Computational complexity theory, Programmable Universal Machine for Assembly and Optimal control.
His Position study combines topics from a wide range of disciplines, such as Control theory, Mathematical optimization, Upper and lower bounds, Robot end effector and Scaling.
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