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Jan Vakula

dblp:151/9704 · 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
Legged, aerial and field robots · 40% Robot navigation and mapping · 40% Motion planning and robot control · 20%

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

TopicWeightPapersLastEvidence papers
Robotics › Legged, aerial and field robots
aerial robots
0.212014
Swarms of micro aerial vehicles stabilized under a visual relative localization · ICRA 2014
Robotics › Robot navigation and mapping
localization
0.212014
Swarms of micro aerial vehicles stabilized under a visual relative localization · ICRA 2014
Robotics › Legged, aerial and field robots › aerial robots › UAV swarm
micro aerial vehicle swarm
0.212014
Swarms of micro aerial vehicles stabilized under a visual relative localization · ICRA 2014
Robotics › Motion planning and robot control
robot control
0.212014
Swarms of micro aerial vehicles stabilized under a visual relative localization · ICRA 2014
Robotics › Robot navigation and mapping › localization › vision-based localization
visual relative localization
0.212014
Swarms of micro aerial vehicles stabilized under a visual relative localization · ICRA 2014

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

visual relative localization · 0.2BOID model · 0.2
YearPublicationVenuePosition
2014 Swarms of micro aerial vehicles stabilized under a visual relative localization
abstract
A stabilization and control technique developed for steering swarms of unmanned micro aerial vehicles is proposed in this paper. The presented approach based on a visual relative localization of swarm particles is designed for utilization of multi-robot teams in real-world dynamic environments. The core of the swarming behaviour is inspired by Reynold's BOID model proposed for 2D simulations of schooling behaviour of fish. The idea of the simple BOID model, with three simple rules: Separation, Alignment and Cohesion, is extended for swarms of quadrotors in this paper. The proposed solution integrates the swarming behaviour with the relative localization and with a stabilization and control mechanism, which respects fast dynamics of unmanned quadrotors. The proposed method aspires to be an enabling technique for deployment of swarms of micro areal vehicles outside laboratories that are equipped with precise positioning systems. The swarming behaviour as well as the possibility of swarm stabilization with the visual relative localization in the control feedback are verified by simulations and partly by an experiment with quadrotors in this paper.
Martin Saska, Jan Vakula, Libor Preucil
ICRA2