FLAGRED – Fuzzy Logic-based Algorithm Generalizing Risk Estimation for Drones

Accurately estimating risk in real-time is essential for ensuring the safety and efficiency of many applications involving autonomous robot systems. This paper presents a novel, generalizable algorithm for the real-time estimation of risks created by external disturbances on multirotors. Unlike conv...

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Main Authors Hovington, Samuel, Petit, Louis, Stratford, Sophie, Hamelin, Philippe, Lussier-Desbiens, Alexis, Ferland, Francois
Format Journal Article
LanguageEnglish
Published 06.02.2024
Subjects
Computer Science - Robotics
Online AccessGet full text
DOI10.48550/arxiv.2402.04518

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Abstract Accurately estimating risk in real-time is essential for ensuring the safety and efficiency of many applications involving autonomous robot systems. This paper presents a novel, generalizable algorithm for the real-time estimation of risks created by external disturbances on multirotors. Unlike conventional approaches, our method requires no additional sensors, accurate drone models, or large datasets. It employs motor command data in a fuzzy logic system, overcoming barriers to real-world implementation. Inherently adaptable, it utilizes fundamental drone characteristics, making it applicable to diverse drone models. The efficiency of the algorithm has been confirmed through comprehensive real-world testing on various platforms. It proficiently discerned between high and low-risk scenarios resulting from diverse wind disturbances and varying thrust-to-weight ratios. The algorithm surpassed the widely-recognized ArduCopter wind estimation algorithm in performance and demonstrated its capability to promptly detect brief gusts.
AbstractList Accurately estimating risk in real-time is essential for ensuring the safety and efficiency of many applications involving autonomous robot systems. This paper presents a novel, generalizable algorithm for the real-time estimation of risks created by external disturbances on multirotors. Unlike conventional approaches, our method requires no additional sensors, accurate drone models, or large datasets. It employs motor command data in a fuzzy logic system, overcoming barriers to real-world implementation. Inherently adaptable, it utilizes fundamental drone characteristics, making it applicable to diverse drone models. The efficiency of the algorithm has been confirmed through comprehensive real-world testing on various platforms. It proficiently discerned between high and low-risk scenarios resulting from diverse wind disturbances and varying thrust-to-weight ratios. The algorithm surpassed the widely-recognized ArduCopter wind estimation algorithm in performance and demonstrated its capability to promptly detect brief gusts.
Author Petit, Louis
Lussier-Desbiens, Alexis
Stratford, Sophie
Hovington, Samuel
Hamelin, Philippe
Ferland, Francois
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BackLink https://doi.org/10.48550/arXiv.2402.04518$$DView paper in arXiv
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Snippet Accurately estimating risk in real-time is essential for ensuring the safety and efficiency of many applications involving autonomous robot systems. This paper...
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Title FLAGRED – Fuzzy Logic-based Algorithm Generalizing Risk Estimation for Drones
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