Corrections to “a Sim-to-Real Deep Learning-Based Framework for Autonomous Nano-Drone Racing”

Authors

Gabriele Abbate (ORCID: https://orcid.org/0000-0001-7797-3861)
Lorenzo Lamberti (ORCID: https://orcid.org/0000-0003-1659-618X)
Agata Barciś (ORCID: https://orcid.org/0000-0003-1444-6719)
Michał Barciś (ORCID: https://orcid.org/0000-0001-6776-2913)
Francesco Conti (ORCID: https://orcid.org/0000-0002-7924-933X)
Elia Cereda (ORCID: https://orcid.org/0000-0001-8888-3538)
Alessandro Giusti (ORCID: https://orcid.org/0000-0003-1240-0768)
Victor Javier Kartsch Morinigo (ORCID: https://orcid.org/0000-0003-3325-6347)
Daniele Palossi (ORCID: https://orcid.org/0000-0003-4487-0836)
Lorenzo Bellone

Institutions

University of Applied Sciences and Arts of Southern Switzerland (CH)
Dalle Molle Institute for Artificial Intelligence Research (CH)
Technology Innovation Institute (AE)
Robotics Research (United States) (US)
University of Bologna (IT)

Publication Details

Journal: IEEE Robotics and Automation Letters
Published: 2024-08-26
DOI: https://doi.org/10.1109/lra.2024.3442628
Primary Topic: Robotic Path Planning Algorithms
Type: article
Field-Weighted Citation Impact: 0.00

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article

Corrections to “a Sim-to-Real Deep Learning-Based Framework for Autonomous Nano-Drone Racing”

Gabriele Abbate, Lorenzo Lamberti, Agata Barciś, Michał Barciś et al.

Aug 26, 2024

IEEE Robotics and Automation Letters

Robotic Path Planning Algorithms

article

Corrections to “a Sim-to-Real Deep Learning-Based Framework for Autonomous Nano-Drone Racing”

Gabriele Abbate, Lorenzo Lamberti, Agata Barciś, Michał Barciś, Francesco Conti, Elia Cereda, Alessandro Giusti, Victor Javier Kartsch Morinigo, Daniele Palossi, Lorenzo Bellone

August 26, 2024article en

Abstract

No abstract available for this paper.

IEEE Robotics and Automation LettersVol. 9(10)

University of Applied Sciences and Arts of Southern Switzerland (CH), Dalle Molle Institute for Artificial Intelligence Research (CH), Technology Innovation Institute (AE), Robotics Research (United States) (US), University of Bologna (IT)

Openalex Percentile: Top 87%

Robotic Path Planning Algorithms

DOI Source OpenAlex

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Corrections to “a Sim-to-Real Deep Learning-Based Framework for Autonomous Nano-Drone Racing” — Gabriele Abbate, Lorenzo Lamberti, et al. · IEEE Robotics and Automation Letters (2024) | TGRS Research Map | TGRS