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Dzenis Beganovic

dblp:151/9574 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2014
—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 · 81% Legged, aerial and field robots · 19%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › grasping
robotic gripper
0.212014
Experimental evaluation of adhesive technologies for robotic grippers on micro-rough surfaces · ICRA 2014
Robotics › Robot manipulation › robot design › manipulator design
adhesion mechanism
0.112014
Experimental evaluation of adhesive technologies for robotic grippers on micro-rough surfaces · ICRA 2014
Robotics › Legged, aerial and field robots › field robotics
climbing robot
0.112014
Experimental evaluation of adhesive technologies for robotic grippers on micro-rough surfaces · ICRA 2014

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

electrostatic adhesion · 0.2directional dry adhesion · 0.2
YearPublicationVenuePosition
2014 Experimental evaluation of adhesive technologies for robotic grippers on micro-rough surfaces
abstract
This paper presents the performance of a newly developed adhesive that combines an electrostatic adhesive with a directional dry (gecko-like) adhesive. The focus is on the adhesive's performance on micro-rough surfaces, which has a large number of applications in robotic mobility and manipulation such as climbing, perching, and grasping. Performance was characterized using shear/normal adhesion pressure limit curves and comparing the new hybrid adhesive to each individual adhesive mechanism and a control. Results show that the electrostatic directional dry adhesive generally performs better than a directional dry adhesive, but that on several surfaces, an electrostatic adhesive with no fibrillar mechanism performs the best. Additionally, the paper introduces a new mechanism that maintains an adhesive's compliance on micro-rough surfaces while transmitting shear and normal forces to a rigid structure. The mechanism is experimentally compared to a rigid backing and a gecko-like hierarchical suspension layer. Results show that the mechanism performs the best when subjected to mainly normal loads, but a hierarchical suspension handles shear loads better.
Donald Ruffatto, Dzenis Beganovic, Aaron Parness, Matthew Spenko
ICRA2