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Frank Bonnet

dblp:160/0929 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · unresolved

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
Video understanding and tracking · 61% Motion planning and robot control · 30% Legged, aerial and field robots · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
multi-robot control
0.312017
Multi-robot control and tracking framework for bio-hybrid systems with closed-loop interaction · ICRA 2017
Computer vision › Video understanding and tracking
object tracking
0.312017
Multi-robot control and tracking framework for bio-hybrid systems with closed-loop interaction · ICRA 2017
Computer vision › Video understanding and tracking › object tracking › efficient tracking
real-time tracking
0.312017
Multi-robot control and tracking framework for bio-hybrid systems with closed-loop interaction · ICRA 2017
Robotics › Legged, aerial and field robots
bio-inspired robot
0.112017
Multi-robot control and tracking framework for bio-hybrid systems with closed-loop interaction · ICRA 2017

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

closed-loop interaction · 0.3bio-mimetic model · 0.3
YearPublicationVenuePosition
2017 Multi-robot control and tracking framework for bio-hybrid systems with closed-loop interaction
abstract
Bio-mimetic robots can interact with groups of animals in bio-hybrid systems to study their behaviour by producing calibrated stimuli and by analysing their responses. Integrating a group of robots into a group of animals to mimic their behaviour is challenging, both in terms of robotic hardware design and robot control. In particular, the robots must be able to react in real-time to the animal changes of behaviour. This implies the need to adequately track and identify animal behaviour. In this paper, we present a novel framework to control several bio-mimetic robots to integrate into groups of fish. Our framework is able to track the position of the fish and robots in real-time. The robots are driven by a bio-mimetic model of fish behaviour from the literature. We show that our multi-robot system can successfully integrate into groups of fish with closed-loop interactions between the robots and the fish.
Frank Bonnet, Leo Cazenille, Alexey Gribovskiy, José Halloy, Francesco Mondada
ICRA1