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A Unified NMPC Scheme for MAVs Navigation With 3D Collision Avoidance Under Position Uncertainty
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: 0000-0003-3922-1735
Department of Computer Science, University of Massachusetts Lowell, Lowell, MA 01854 USA.
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2020 (English)In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 5, no 4, p. 5740-5747Article in journal (Refereed) Published
Abstract [en]

This letter proposes a novel Nonlinear Model Predictive Control (NMPC) framework for Micro Aerial Vehicle (MAV) autonomous navigation in indoor enclosed environments. The introduced framework allows us to consider the nonlinear dynamics of MAVs, nonlinear geometric constraints, while guarantees real-time performance. Our first contribution is to reveal underlying planes within a 3D point cloud, obtained from a 3D lidar scanner, by designing an efficient subspace clustering method. The second contribution is to incorporate the extracted information into the nonlinear constraints of NMPC for avoiding collisions. Our third contribution focuses on making the controller robust by considering the uncertainty of localization in NMPC using Shannon's entropy to define the weights involved in the optimization process. This strategy enables us to track position or velocity references or none in the event of losing track of position or velocity estimations. As a result, the collision avoidance constraints are defined in the local coordinates of the MAV and it remains active and guarantees collision avoidance, despite localization uncertainties, e.g., position estimation drifts. The efficacy of the suggested framework has been evaluated using various simulations in the Gazebo environment.

Place, publisher, year, edition, pages
IEEE, 2020. Vol. 5, no 4, p. 5740-5747
Keywords [en]
Aerial systems: applications, collision avoidance, autonomous vehicle navigation, segmentation and categorization, optimization and optimal control
National Category
Robotics
Research subject
Robotics and Artificial Intelligence
Identifiers
URN: urn:nbn:se:ltu:diva-80558DOI: 10.1109/LRA.2020.3010485ISI: 000554891100016Scopus ID: 2-s2.0-85089886124OAI: oai:DiVA.org:ltu-80558DiVA, id: diva2:1460997
Note

Validerad;2020;Nivå 2;2020-08-25 (alebob)

Available from: 2020-08-25 Created: 2020-08-25 Last updated: 2025-02-05Bibliographically approved

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Mansouri, Sina SharifKanellakis, ChristoforosLindqvist, BjörnNikolakopoulos, George

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