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Jeff Gau

dblp:324/6288 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · unresolved

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
Motion planning and robot control · 67% Legged, aerial and field robots · 33%

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

TopicWeightPapersLastEvidence papers
Robotics › Legged, aerial and field robots › aerial robots
flapping-wing robot
0.612022
Autonomous Actuation of Flapping Wing Robots Inspired by Asynchronous Insect Muscle · ICRA 2022
Robotics › Motion planning and robot control › robot control › nonlinear control
limit cycle control
0.612022
Autonomous Actuation of Flapping Wing Robots Inspired by Asynchronous Insect Muscle · ICRA 2022
Robotics › Motion planning and robot control
robot control
0.612022
Autonomous Actuation of Flapping Wing Robots Inspired by Asynchronous Insect Muscle · ICRA 2022

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

robophysical model · 0.6feedback control law · 0.6delayed stretch-activation · 0.6
YearPublicationVenuePosition
2022 Autonomous Actuation of Flapping Wing Robots Inspired by Asynchronous Insect Muscle
abstract
In most instances, flapping wing robots have emulated the “synchronous” actuation of insects in which the wingbeat timing is generated from a time-dependent, rhythmic signal. The internal dynamics of asynchronous insect flight muscle enable high-frequency, adaptive wingbeats with minimal direct neural control. In this paper, we investigate how the delayed stretch-activation (dSA) response of asynchronous insect flight muscle can be transformed into a feedback control law for flapping wing robots that results in stable limit cycle wingbeats. We first demonstrate - in theory and simulation - the mechanism by which asynchronous wingbeats self-excite. Then, we implement the feedback law on a dynamically-scaled robophysical model as well as on an insect-scale robotic flapping wing. Experiments on large- and small-scale robots demonstrate good agreement with the theory results and highlight how dSA parameters govern wingbeat amplitude and frequency. Lastly, we demonstrate that asynchronous actuation has several advantages over synchronous actuation schemes, including the ability to rapidly adapt or halt wingbeats in response to external loads or collisions through low-level feedback control.
Jeff Gau, Simon Sponberg, Nick Gravish
ICRA2