Cooperative Multi-UAV Conflict Avoidance Planning in a Complex Urban Environment

Trajectory planning is of great value and yet challenging for multirotor unmanned aerial vehicle (UAV) applications in a complex urban environment, mainly due to the complexities of handling cluttered obstacles. The problem further complicates itself in the context of autonomous multi-UAV trajectory...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Kaiping Wang [verfasserIn] Mingzhu Song [verfasserIn] Meng Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	multi-UAV cooperative trajectory planning conflict avoidance holding time

Übergeordnetes Werk:	In: Sustainability - MDPI AG, 2009, 13(2021), 12, p 6807
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:12, p 6807

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/su13126807

Katalog-ID:	DOAJ03532967X

Internformat


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520			\|a Trajectory planning is of great value and yet challenging for multirotor unmanned aerial vehicle (UAV) applications in a complex urban environment, mainly due to the complexities of handling cluttered obstacles. The problem further complicates itself in the context of autonomous multi-UAV trajectory planning considering conflict avoidance for future city applications. To tackle this problem, this paper introduces the multi-UAV cooperative trajectory planning (MCTP) problem, and proposes a bilevel model for the problem. The upper level is modeled as an extended multiple traveling salesman problem, aiming at generating trajectories based on heuristic framework for multi-UAV task allocation and scheduling and meanwhile considering UAV kinodynamic properties. The lower level is modeled as a holding time assignment problem to avoid possible spatiotemporal trajectory conflicts, where conflict time difference is analyzed based on the proposed state-time graph method. Numerical studies are conducted in both a 1 <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mi<km</mi<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< virtual city and 12 <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mi<km</mi<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< real city with a set of tasks and obstacles settings. The results show that the proposed model is capable of planning trajectories for multi-UAV from the system-level perspective based on the proposed method.
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spelling	10.3390/su13126807 doi (DE-627)DOAJ03532967X (DE-599)DOAJ2c99b47382e9421383bcd49706527afc DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Kaiping Wang verfasserin aut Cooperative Multi-UAV Conflict Avoidance Planning in a Complex Urban Environment 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Trajectory planning is of great value and yet challenging for multirotor unmanned aerial vehicle (UAV) applications in a complex urban environment, mainly due to the complexities of handling cluttered obstacles. The problem further complicates itself in the context of autonomous multi-UAV trajectory planning considering conflict avoidance for future city applications. To tackle this problem, this paper introduces the multi-UAV cooperative trajectory planning (MCTP) problem, and proposes a bilevel model for the problem. The upper level is modeled as an extended multiple traveling salesman problem, aiming at generating trajectories based on heuristic framework for multi-UAV task allocation and scheduling and meanwhile considering UAV kinodynamic properties. The lower level is modeled as a holding time assignment problem to avoid possible spatiotemporal trajectory conflicts, where conflict time difference is analyzed based on the proposed state-time graph method. Numerical studies are conducted in both a 1 <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mi<km</mi<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< virtual city and 12 <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mi<km</mi<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< real city with a set of tasks and obstacles settings. The results show that the proposed model is capable of planning trajectories for multi-UAV from the system-level perspective based on the proposed method. multi-UAV cooperative trajectory planning conflict avoidance holding time Environmental effects of industries and plants Renewable energy sources Environmental sciences Mingzhu Song verfasserin aut Meng Li verfasserin aut In Sustainability MDPI AG, 2009 13(2021), 12, p 6807 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:13 year:2021 number:12, p 6807 https://doi.org/10.3390/su13126807 kostenfrei https://doaj.org/article/2c99b47382e9421383bcd49706527afc kostenfrei https://www.mdpi.com/2071-1050/13/12/6807 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 12, p 6807
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language	English
source	In Sustainability 13(2021), 12, p 6807 volume:13 year:2021 number:12, p 6807
sourceStr	In Sustainability 13(2021), 12, p 6807 volume:13 year:2021 number:12, p 6807
format_phy_str_mv	Article
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topic_facet	multi-UAV cooperative trajectory planning conflict avoidance holding time Environmental effects of industries and plants Renewable energy sources Environmental sciences
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container_title	Sustainability
authorswithroles_txt_mv	Kaiping Wang @@aut@@ Mingzhu Song @@aut@@ Meng Li @@aut@@
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callnumber-first	T - Technology
author	Kaiping Wang
spellingShingle	Kaiping Wang misc TD194-195 misc TJ807-830 misc GE1-350 misc multi-UAV misc cooperative trajectory planning misc conflict avoidance misc holding time misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences Cooperative Multi-UAV Conflict Avoidance Planning in a Complex Urban Environment
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topic_title	TD194-195 TJ807-830 GE1-350 Cooperative Multi-UAV Conflict Avoidance Planning in a Complex Urban Environment multi-UAV cooperative trajectory planning conflict avoidance holding time
topic	misc TD194-195 misc TJ807-830 misc GE1-350 misc multi-UAV misc cooperative trajectory planning misc conflict avoidance misc holding time misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
topic_unstemmed	misc TD194-195 misc TJ807-830 misc GE1-350 misc multi-UAV misc cooperative trajectory planning misc conflict avoidance misc holding time misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
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title	Cooperative Multi-UAV Conflict Avoidance Planning in a Complex Urban Environment
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title_full	Cooperative Multi-UAV Conflict Avoidance Planning in a Complex Urban Environment
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title_sort	cooperative multi-uav conflict avoidance planning in a complex urban environment
callnumber	TD194-195
title_auth	Cooperative Multi-UAV Conflict Avoidance Planning in a Complex Urban Environment
abstract	Trajectory planning is of great value and yet challenging for multirotor unmanned aerial vehicle (UAV) applications in a complex urban environment, mainly due to the complexities of handling cluttered obstacles. The problem further complicates itself in the context of autonomous multi-UAV trajectory planning considering conflict avoidance for future city applications. To tackle this problem, this paper introduces the multi-UAV cooperative trajectory planning (MCTP) problem, and proposes a bilevel model for the problem. The upper level is modeled as an extended multiple traveling salesman problem, aiming at generating trajectories based on heuristic framework for multi-UAV task allocation and scheduling and meanwhile considering UAV kinodynamic properties. The lower level is modeled as a holding time assignment problem to avoid possible spatiotemporal trajectory conflicts, where conflict time difference is analyzed based on the proposed state-time graph method. Numerical studies are conducted in both a 1 <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mi<km</mi<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< virtual city and 12 <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mi<km</mi<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< real city with a set of tasks and obstacles settings. The results show that the proposed model is capable of planning trajectories for multi-UAV from the system-level perspective based on the proposed method.
abstractGer	Trajectory planning is of great value and yet challenging for multirotor unmanned aerial vehicle (UAV) applications in a complex urban environment, mainly due to the complexities of handling cluttered obstacles. The problem further complicates itself in the context of autonomous multi-UAV trajectory planning considering conflict avoidance for future city applications. To tackle this problem, this paper introduces the multi-UAV cooperative trajectory planning (MCTP) problem, and proposes a bilevel model for the problem. The upper level is modeled as an extended multiple traveling salesman problem, aiming at generating trajectories based on heuristic framework for multi-UAV task allocation and scheduling and meanwhile considering UAV kinodynamic properties. The lower level is modeled as a holding time assignment problem to avoid possible spatiotemporal trajectory conflicts, where conflict time difference is analyzed based on the proposed state-time graph method. Numerical studies are conducted in both a 1 <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mi<km</mi<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< virtual city and 12 <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mi<km</mi<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< real city with a set of tasks and obstacles settings. The results show that the proposed model is capable of planning trajectories for multi-UAV from the system-level perspective based on the proposed method.
abstract_unstemmed	Trajectory planning is of great value and yet challenging for multirotor unmanned aerial vehicle (UAV) applications in a complex urban environment, mainly due to the complexities of handling cluttered obstacles. The problem further complicates itself in the context of autonomous multi-UAV trajectory planning considering conflict avoidance for future city applications. To tackle this problem, this paper introduces the multi-UAV cooperative trajectory planning (MCTP) problem, and proposes a bilevel model for the problem. The upper level is modeled as an extended multiple traveling salesman problem, aiming at generating trajectories based on heuristic framework for multi-UAV task allocation and scheduling and meanwhile considering UAV kinodynamic properties. The lower level is modeled as a holding time assignment problem to avoid possible spatiotemporal trajectory conflicts, where conflict time difference is analyzed based on the proposed state-time graph method. Numerical studies are conducted in both a 1 <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mi<km</mi<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< virtual city and 12 <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mi<km</mi<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< real city with a set of tasks and obstacles settings. The results show that the proposed model is capable of planning trajectories for multi-UAV from the system-level perspective based on the proposed method.
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title_short	Cooperative Multi-UAV Conflict Avoidance Planning in a Complex Urban Environment
url	https://doi.org/10.3390/su13126807 https://doaj.org/article/2c99b47382e9421383bcd49706527afc https://www.mdpi.com/2071-1050/13/12/6807 https://doaj.org/toc/2071-1050
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up_date	2024-07-03T14:19:10.844Z
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