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Nishant Pol

dblp:188/3266 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot manipulation · 50% Motion planning and robot control · 50%
Human-computer interaction and pervasive computing
1 paper
Wearable and physiological sensing · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › actuation
pneumatic artificial muscle
0.312017
Design, modeling, and control of pneumatic artificial muscles with integrated soft sensing · ICRA 2017
Robotics › Motion planning and robot control › robot control › feedback control
position feedback control
0.312017
Design, modeling, and control of pneumatic artificial muscles with integrated soft sensing · ICRA 2017
Robotics › Motion planning and robot control
robot control
0.312017
Design, modeling, and control of pneumatic artificial muscles with integrated soft sensing · ICRA 2017
Robotics › Robot manipulation › soft robotics
soft actuator
0.312017
Design, modeling, and control of pneumatic artificial muscles with integrated soft sensing · ICRA 2017
Wearable and physiological sensing › flexible electronics
soft sensors
0.112017
Design, modeling, and control of pneumatic artificial muscles with integrated soft sensing · ICRA 2017

Methods — techniques the papers use, named apart from their topics

sliding mode control · 0.6liquid metal direct writing · 0.63d manufacturing · 0.6
YearPublicationVenuePosition
2017 Design, modeling, and control of pneumatic artificial muscles with integrated soft sensing
abstract
Presented are techniques for designing, modeling, and control of reliable pneumatic artificial muscle actuators with integrated low profile sensors for position feedback. The sensor is fabricated through a three-dimensional manufacturing process based on a modified lathe approach for controlling viscous and viscoelastic materials as well as on direct writing of liquid metal. Next, a new precision pneumatic muscle design and its integration with the sensor is illustrated. A theoretical model and experimental characterization of the muscle-sensor package are presented with high correlation and repeatability. Finally, a position feedback sliding mode controller is implemented with a position error of <0.9% of maximum muscle contraction.
Jonathan P. King, Luis E. Valle, Nishant Pol, Yong-Lae Park
ICRA3