H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Engineering and Technology H-index 64 Citations 14,356 193 World Ranking 451 National Ranking 202

Overview

What is he best known for?

The fields of study he is best known for:

  • Composite material
  • Mechanical engineering
  • Artificial intelligence

Composite material, Actuator, Robot, Adhesive and Adhesion are his primary areas of study. His Composite material research integrates issues from Thin film and Response time. The concepts of his Actuator study are interwoven with issues in Piezoelectricity, Fiber-reinforced composite, Structural engineering and Mechanical engineering.

His Robot study integrates concerns from other disciplines, such as Slip, Simulation and Control theory. His Adhesive research incorporates elements of Substrate, Perpendicular, Surface roughness, Microstructure and Dielectrophoresis. The various areas that Ronald S. Fearing examines in his Adhesion study include Electrokinetic phenomena, Synthetic setae and Classical mechanics.

His most cited work include:

  • Adhesive force of a single gecko foot-hair (1902 citations)
  • Evidence for van der Waals adhesion in gecko setae (1383 citations)
  • Nanowire active-matrix circuitry for low-voltage macroscale artificial skin (930 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Robot, Simulation, Actuator, Control theory and Composite material. His Robot research is multidisciplinary, incorporating elements of Mechanical engineering and Kinematics. Ronald S. Fearing has included themes like Piezoelectricity, Acoustics, Structural engineering and Layer in his Actuator study.

His work deals with themes such as Work and Aerodynamics, which intersect with Control theory. Adhesive, Adhesion, Microstructure, Microfiber and Elastic modulus are among the areas of Composite material where the researcher is concentrating his efforts. His research brings together the fields of Nanotechnology and Adhesion.

He most often published in these fields:

  • Robot (38.06%)
  • Simulation (22.67%)
  • Actuator (22.67%)

What were the highlights of his more recent work (between 2013-2021)?

  • Robot (38.06%)
  • Control theory (18.62%)
  • Simulation (22.67%)

In recent papers he was focusing on the following fields of study:

His scientific interests lie mostly in Robot, Control theory, Simulation, Actuator and Artificial intelligence. His Robot research is multidisciplinary, incorporating perspectives in Gait and Work. Ronald S. Fearing has researched Control theory in several fields, including Kinematics and Aerodynamics.

His Simulation research focuses on Robot locomotion and how it connects with Mechanical engineering. His Actuator study combines topics in areas such as Layer, Planar and Voltage. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Computer vision.

Between 2013 and 2021, his most popular works were:

  • Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning (430 citations)
  • Photoactuators and motors based on carbon nanotubes with selective chirality distributions (190 citations)
  • Wearable Microfluidic Diaphragm Pressure Sensor for Health and Tactile Touch Monitoring (173 citations)

In his most recent research, the most cited papers focused on:

  • Mechanical engineering
  • Composite material
  • Artificial intelligence

Robot, Control theory, Artificial intelligence, Simulation and Reinforcement learning are his primary areas of study. His studies in Robot integrate themes in fields like Mechanical engineering, Control engineering, Exoskeleton, Nonlinear system and Robustness. His Control theory research includes themes of Kinematics and Acceleration.

His studies examine the connections between Artificial intelligence and genetics, as well as such issues in Computer vision, with regards to Tree traversal and Body roll. His study in Simulation is interdisciplinary in nature, drawing from both Robotics, Tactile sensor, Soft robotics, Legged robot and DC motor. His Actuator study combines topics from a wide range of disciplines, such as Chromatic scale, Carbon nanotube and Polymer.

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.

Top Publications

Adhesive force of a single gecko foot-hair

Kellar Autumn;Yiching A. Liang;S. Tonia Hsieh;Wolfgang Zesch.
Nature (2000)

2805 Citations

Evidence for van der Waals adhesion in gecko setae

Kellar Autumn;Metin Sitti;Yiching A. Liang;Anne M. Peattie.
Proceedings of the National Academy of Sciences of the United States of America (2002)

1950 Citations

Nanowire active-matrix circuitry for low-voltage macroscale artificial skin

Kuniharu Takei;Toshitake Takahashi;Toshitake Takahashi;Johnny C. Ho;Johnny C. Ho;Hyunhyub Ko.
Nature Materials (2010)

1141 Citations

Towards a 3g crawling robot through the integration of microrobot technologies

R. Sahai;S. Avadhanula;R. Groff;E. Steltz.
international conference on robotics and automation (2006)

639 Citations

Survey of sticking effects for micro parts handling

R.S. Fearing.
intelligent robots and systems (1995)

629 Citations

Synthetic gecko foot-hair micro/nano-structures as dry adhesives

Metin Sitti;Ronald S. Fearing.
Journal of Adhesion Science and Technology (2003)

621 Citations

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

Anusha Nagabandi;Gregory Kahn;Ronald S. Fearing;Sergey Levine.
international conference on robotics and automation (2018)

481 Citations

Optically- and thermally-responsive programmable materials based on carbon nanotube-hydrogel polymer composites

Xiaobo Zhang;Cary L. Pint;Min Hyung Lee;Bryan Edward Schubert.
Nano Letters (2011)

402 Citations

Microrobot Design Using Fiber Reinforced Composites

R. J. Wood;S. Avadhanula;R. Sahai;E. Steltz.
Journal of Mechanical Design (2008)

374 Citations

Tactile sensing mechanisms

R. S. Fearing.
The International Journal of Robotics Research (1990)

350 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

If you think any of the details on this page are incorrect, let us know.

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