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Geometry Aware NMPC Scheme for Morphing Quadrotor Navigation in Restricted Entrances
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-6415-6982
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-7631-002x
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-8870-6718
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-0126-1897
2021 (English)In: 2021 European Control Conference (ECC), IEEE, 2021, p. 1597-1603Conference paper, Published paper (Refereed)
Abstract [en]

Geometry-morphing Micro Aerial Vehicles (MAVs) are gaining more attention lately, since, their ability to modify their geometric morphology in-flight increases their versatility while expanding their application range. In this novel research field, most of the works focus on the platform design and on the low-level control for maintaining stability after changing its shape. Nevertheless, another aspect of geometry morphing MAVs is the association of its morphology with respect to the shape and structure of the environment. In this article, we propose a novel Nonlinear Model Predictive Control (NMPC) structure that modifies the morphology of a quadrotor based on the environment entrances' geometrical shape. The proposed method considers restricted entrances as a constraint in the NMPC and modifies the arm configuration of the MAV to provide a collision-free path from the initial position to the desired goal while passing through the entrance. Multiple simulation results present the performance and efficiency of the proposed scheme in scenarios where the quadrotor is commanded to pass through narrow entrances.

Place, publisher, year, edition, pages
IEEE, 2021. p. 1597-1603
National Category
Control Engineering
Research subject
Robotics and Artificial Intelligence
Identifiers
URN: urn:nbn:se:ltu:diva-89571DOI: 10.23919/ECC54610.2021.9655205ISI: 000768455200233Scopus ID: 2-s2.0-85124909216OAI: oai:DiVA.org:ltu-89571DiVA, id: diva2:1648587
Conference
2021 European Control Conference, ECC 2021, Virtual Event / Delft, The Netherlands, June 29 - July 2, 2021
Note

ISBN för värdpublikation: 978-9-4638-4236-5; 978-1-6654-7945-5

Available from: 2022-03-31 Created: 2022-03-31 Last updated: 2022-04-21Bibliographically approved

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Papadimitriou, AndreasMansouri, Sina SharifKanellakis, ChristoforosNikolakopoulos, George

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