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Livio Giacomini

dblp:313/9210 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—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
Motion planning and robot control · 83% Legged, aerial and field robots · 17%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control
impedance control
0.612022
Energy Tank-Based Policies for Robust Aerial Physical Interaction with Moving Objects · ICRA 2022
Robotics › Motion planning and robot control › robot control
passivity-based control
0.612022
Energy Tank-Based Policies for Robust Aerial Physical Interaction with Moving Objects · ICRA 2022
Robotics › Motion planning and robot control
robot control
0.612022
Energy Tank-Based Policies for Robust Aerial Physical Interaction with Moving Objects · ICRA 2022
Robotics › Legged, aerial and field robots › aerial robots
aerial physical interaction
0.212022
Energy Tank-Based Policies for Robust Aerial Physical Interaction with Moving Objects · ICRA 2022
Robotics › Legged, aerial and field robots
aerial robots
0.212022
Energy Tank-Based Policies for Robust Aerial Physical Interaction with Moving Objects · ICRA 2022

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

momentum-based wrench estimation · 0.6energy tank · 0.6disturbance observer · 0.6
YearPublicationVenuePosition
2022 Energy Tank-Based Policies for Robust Aerial Physical Interaction with Moving Objects
abstract
Although manipulation capabilities of aerial robots greatly improved in the last decade, only few works addressed the problem of aerial physical interaction with dynamic environments, proposing strongly model-based approaches. However, in real scenarios, modeling the environment with high accuracy is often impossible. In this work, we aim at developing a control framework for Omnidirectional Micro Aerial Vehicles (OMAVs) for reliable physical interaction tasks with articulated and movable objects in the presence of possibly unforeseen disturbances, and without relying on an accurate model of the environment. Inspired by previous applications of energy-based controllers for physical interaction, we propose a passivity-based impedance and wrench tracking controller in combination with a momentum-based wrench estimator. This is combined with an energytank framework to guarantee the stability of the system, while energy and power flow-based adaptation policies are deployed to enable safe interaction with any type of passive environment. The control framework provides formal guarantees of stability, which is validated in practice considering the challenging task of pushing a cart of unknown mass, moving on a surface of unknown friction, as well as subjected to unknown disturbances. For this scenario, we present, evaluate and discuss three different policies.
Maximilian Brunner, Livio Giacomini, Roland Siegwart, Marco Tognon
ICRA2