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Yannis Mantzouratos

dblp:164/8308 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

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 · 77% Motion planning and robot control · 23%
Theoretical computer science
1 paper
Graph algorithms and graph theory · 100%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › robot design
modular robot design
0.212015
On embeddability of modular robot designs · ICRA 2015
Graph algorithms and graph theory
graph embedding
0.212015
On embeddability of modular robot designs · ICRA 2015
Robotics › Motion planning and robot control › robot kinematics
kinematic constraints
0.112015
On embeddability of modular robot designs · ICRA 2015

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

maximum cardinality matching · 0.4dynamic programming · 0.4
YearPublicationVenuePosition
2015 On embeddability of modular robot designs
abstract
We address the problem of detecting embeddability of modular robots: namely, to decide automatically whether a given modular robot design can simulate the functionality of a seemingly different design. To that end, we introduce a novel graph representation for modular robots and formalize the notion of embedding through topological and kinematic conditions. Based on that, we develop an algorithm that decides embeddability when the two involved designs have tree topologies. Our algorithm performs two passes and involves dynamic programming and maximum cardinality matching. We demonstrate our approach on real modular robots and show that we can detect embeddability of complex designs efficiently.
Yannis Mantzouratos, Tarik Tosun, Sanjeev Khanna, Mark Yim
ICRA1