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Michael J. Mathew

dblp:207/8048 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

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 · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
contact modeling
0.512021
Polyhedral Friction Cone Estimator for Object Manipulation · ICRA 2021

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

polyhedral estimation · 0.5
YearPublicationVenuePosition
2021 Polyhedral Friction Cone Estimator for Object Manipulation
abstract
A polyhedral friction cone is a set of reaction wrenches that an object can experience whilst in contact with its environment. This polyhedron is a powerful tool to control an object’s motion and interaction with the environment. It can be derived analytically, upon knowledge of object and environment geometries, contact point locations and friction coefficients. We propose to estimate the polyhedral friction cone so that a priori knowledge of these quantities is no longer required. Additionally, we introduce a solution to transform the estimated friction cone to avoid re-estimation while the object moves. We present an analysis of the estimated polyhedral friction cone and demonstrate its application for manipulating an object in simulation and with a real robot.
Morteza Azad, Silvia Cruciani, Michael J. Mathew, Graham E. Deacon, Guillaume de Chambrier
ICRA3