Trajectory Generation Using Semidefinite Programming For Multi-Rotors

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Abstract

Trajectory generation is one of the most popular techniques for multi-rotors to achieve autonomous navigation capabilities. In this paper, we present an optimization-based approach for generating smooth, feasible and collision-free trajectories. Our method does not require a prior proper time allocation but attempts to optimize time allocation automatically. This method first optimizes trajectories subjected to dynamic constraints and 3D corridor constraints using a quadratic program. Then time allocation is optimized as a semidefinite program by leveraging semidefinite relaxation. A feasible solution is obtained using the rank-one approximation and used to update previous time allocation. This method run iteratively until a feasible trajectory is obtained. Our approach is validated with simulation results. Experimental results show the proposed method ensures to generate feasible trajectories in clutter environments.
Original languageEnglish
Title of host publicationProceedings of the European Control Conference 2019
PublisherIEEE
Publication date2019
Pages2577-2582
ISBN (Electronic)9783907144008 , 9783907144015
DOIs
Publication statusPublished - 2019
EventEuropean Control Conference 2019 - Naples, Italy
Duration: 25 Jun 201928 Jun 2019

Conference

ConferenceEuropean Control Conference 2019
CountryItaly
CityNaples
Period25/06/201928/06/2019

Cite this

Hu, Xiao ; Olesen, Daniel ; Knudsen, Per. / Trajectory Generation Using Semidefinite Programming For Multi-Rotors. Proceedings of the European Control Conference 2019. IEEE, 2019. pp. 2577-2582
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title = "Trajectory Generation Using Semidefinite Programming For Multi-Rotors",
abstract = "Trajectory generation is one of the most popular techniques for multi-rotors to achieve autonomous navigation capabilities. In this paper, we present an optimization-based approach for generating smooth, feasible and collision-free trajectories. Our method does not require a prior proper time allocation but attempts to optimize time allocation automatically. This method first optimizes trajectories subjected to dynamic constraints and 3D corridor constraints using a quadratic program. Then time allocation is optimized as a semidefinite program by leveraging semidefinite relaxation. A feasible solution is obtained using the rank-one approximation and used to update previous time allocation. This method run iteratively until a feasible trajectory is obtained. Our approach is validated with simulation results. Experimental results show the proposed method ensures to generate feasible trajectories in clutter environments.",
author = "Xiao Hu and Daniel Olesen and Per Knudsen",
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doi = "10.23919/ECC.2019.8795662",
language = "English",
pages = "2577--2582",
booktitle = "Proceedings of the European Control Conference 2019",
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Hu, X, Olesen, D & Knudsen, P 2019, Trajectory Generation Using Semidefinite Programming For Multi-Rotors. in Proceedings of the European Control Conference 2019. IEEE, pp. 2577-2582 , European Control Conference 2019, Naples, Italy, 25/06/2019. https://doi.org/10.23919/ECC.2019.8795662

Trajectory Generation Using Semidefinite Programming For Multi-Rotors. / Hu, Xiao; Olesen, Daniel; Knudsen, Per.

Proceedings of the European Control Conference 2019. IEEE, 2019. p. 2577-2582 .

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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AB - Trajectory generation is one of the most popular techniques for multi-rotors to achieve autonomous navigation capabilities. In this paper, we present an optimization-based approach for generating smooth, feasible and collision-free trajectories. Our method does not require a prior proper time allocation but attempts to optimize time allocation automatically. This method first optimizes trajectories subjected to dynamic constraints and 3D corridor constraints using a quadratic program. Then time allocation is optimized as a semidefinite program by leveraging semidefinite relaxation. A feasible solution is obtained using the rank-one approximation and used to update previous time allocation. This method run iteratively until a feasible trajectory is obtained. Our approach is validated with simulation results. Experimental results show the proposed method ensures to generate feasible trajectories in clutter environments.

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