Multi-UAV Cooperative Trajectory Planning Based on FDS-ADEA in Complex Environments

Multi-UAV cooperative trajectory planning (MUCTP) refers to the planning of multiple flyable trajectories based on the location of each UAV and mission point in a complex environment. In the planning process, the complex 3D space structure increases the difficulty of solving the trajectory points, a...
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Autor*in:	Gang Huang [verfasserIn] Min Hu [verfasserIn] Xueying Yang [verfasserIn] Peng Lin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	multi-UAV cooperative trajectory planning 3D feasible domain space adaptive differential evolutionary algorithm key trajectory points code correction method

Übergeordnetes Werk:	In: Drones - MDPI AG, 2018, 7(2023), 1, p 55
Übergeordnetes Werk:	volume:7 ; year:2023 ; number:1, p 55

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/drones7010055

Katalog-ID:	DOAJ081817290

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520			\|a Multi-UAV cooperative trajectory planning (MUCTP) refers to the planning of multiple flyable trajectories based on the location of each UAV and mission point in a complex environment. In the planning process, the complex 3D space structure increases the difficulty of solving the trajectory points, and the mutual constraints of the UAV cooperative constraints can degrade the performance of the planning system. Therefore, to improve the efficiency of MUCTP, this study proposes MUCTP based on feasible domain space and adaptive differential evolution algorithm (FDS-ADEA). The method first constructs a three-dimensional feasible domain space to reduce the complexity of the search space structure; then, the constraints of heterogeneous UAVs are linearly weighted and transformed into a new objective function, and the information of the fitness value is shared in accordance with the adaptive method and the code correction method to improve the search efficiency of the algorithm; finally, the trajectories are smoothed to ensure the flyability of the UAVs performing the mission by combining the cubic B-spline curves. Experiments 1, 2, 3, and 4 validate the proposed algorithm. Simulation results verify that FDS-ADEA has fast convergence, high cooperative capability, and more reasonable planned trajectory sets when processing MUCTP.
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spelling	10.3390/drones7010055 doi (DE-627)DOAJ081817290 (DE-599)DOAJbef3341da91b4942ad97bbd0f7e07624 DE-627 ger DE-627 rakwb eng TL1-4050 Gang Huang verfasserin aut Multi-UAV Cooperative Trajectory Planning Based on FDS-ADEA in Complex Environments 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-UAV cooperative trajectory planning (MUCTP) refers to the planning of multiple flyable trajectories based on the location of each UAV and mission point in a complex environment. In the planning process, the complex 3D space structure increases the difficulty of solving the trajectory points, and the mutual constraints of the UAV cooperative constraints can degrade the performance of the planning system. Therefore, to improve the efficiency of MUCTP, this study proposes MUCTP based on feasible domain space and adaptive differential evolution algorithm (FDS-ADEA). The method first constructs a three-dimensional feasible domain space to reduce the complexity of the search space structure; then, the constraints of heterogeneous UAVs are linearly weighted and transformed into a new objective function, and the information of the fitness value is shared in accordance with the adaptive method and the code correction method to improve the search efficiency of the algorithm; finally, the trajectories are smoothed to ensure the flyability of the UAVs performing the mission by combining the cubic B-spline curves. Experiments 1, 2, 3, and 4 validate the proposed algorithm. Simulation results verify that FDS-ADEA has fast convergence, high cooperative capability, and more reasonable planned trajectory sets when processing MUCTP. multi-UAV cooperative trajectory planning 3D feasible domain space adaptive differential evolutionary algorithm key trajectory points code correction method Motor vehicles. Aeronautics. Astronautics Min Hu verfasserin aut Xueying Yang verfasserin aut Peng Lin verfasserin aut In Drones MDPI AG, 2018 7(2023), 1, p 55 (DE-627)1025498356 2504446X nnns volume:7 year:2023 number:1, p 55 https://doi.org/10.3390/drones7010055 kostenfrei https://doaj.org/article/bef3341da91b4942ad97bbd0f7e07624 kostenfrei https://www.mdpi.com/2504-446X/7/1/55 kostenfrei https://doaj.org/toc/2504-446X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2023 1, p 55
allfields_unstemmed	10.3390/drones7010055 doi (DE-627)DOAJ081817290 (DE-599)DOAJbef3341da91b4942ad97bbd0f7e07624 DE-627 ger DE-627 rakwb eng TL1-4050 Gang Huang verfasserin aut Multi-UAV Cooperative Trajectory Planning Based on FDS-ADEA in Complex Environments 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-UAV cooperative trajectory planning (MUCTP) refers to the planning of multiple flyable trajectories based on the location of each UAV and mission point in a complex environment. In the planning process, the complex 3D space structure increases the difficulty of solving the trajectory points, and the mutual constraints of the UAV cooperative constraints can degrade the performance of the planning system. Therefore, to improve the efficiency of MUCTP, this study proposes MUCTP based on feasible domain space and adaptive differential evolution algorithm (FDS-ADEA). The method first constructs a three-dimensional feasible domain space to reduce the complexity of the search space structure; then, the constraints of heterogeneous UAVs are linearly weighted and transformed into a new objective function, and the information of the fitness value is shared in accordance with the adaptive method and the code correction method to improve the search efficiency of the algorithm; finally, the trajectories are smoothed to ensure the flyability of the UAVs performing the mission by combining the cubic B-spline curves. Experiments 1, 2, 3, and 4 validate the proposed algorithm. Simulation results verify that FDS-ADEA has fast convergence, high cooperative capability, and more reasonable planned trajectory sets when processing MUCTP. multi-UAV cooperative trajectory planning 3D feasible domain space adaptive differential evolutionary algorithm key trajectory points code correction method Motor vehicles. Aeronautics. Astronautics Min Hu verfasserin aut Xueying Yang verfasserin aut Peng Lin verfasserin aut In Drones MDPI AG, 2018 7(2023), 1, p 55 (DE-627)1025498356 2504446X nnns volume:7 year:2023 number:1, p 55 https://doi.org/10.3390/drones7010055 kostenfrei https://doaj.org/article/bef3341da91b4942ad97bbd0f7e07624 kostenfrei https://www.mdpi.com/2504-446X/7/1/55 kostenfrei https://doaj.org/toc/2504-446X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2023 1, p 55
allfieldsGer	10.3390/drones7010055 doi (DE-627)DOAJ081817290 (DE-599)DOAJbef3341da91b4942ad97bbd0f7e07624 DE-627 ger DE-627 rakwb eng TL1-4050 Gang Huang verfasserin aut Multi-UAV Cooperative Trajectory Planning Based on FDS-ADEA in Complex Environments 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-UAV cooperative trajectory planning (MUCTP) refers to the planning of multiple flyable trajectories based on the location of each UAV and mission point in a complex environment. In the planning process, the complex 3D space structure increases the difficulty of solving the trajectory points, and the mutual constraints of the UAV cooperative constraints can degrade the performance of the planning system. Therefore, to improve the efficiency of MUCTP, this study proposes MUCTP based on feasible domain space and adaptive differential evolution algorithm (FDS-ADEA). The method first constructs a three-dimensional feasible domain space to reduce the complexity of the search space structure; then, the constraints of heterogeneous UAVs are linearly weighted and transformed into a new objective function, and the information of the fitness value is shared in accordance with the adaptive method and the code correction method to improve the search efficiency of the algorithm; finally, the trajectories are smoothed to ensure the flyability of the UAVs performing the mission by combining the cubic B-spline curves. Experiments 1, 2, 3, and 4 validate the proposed algorithm. Simulation results verify that FDS-ADEA has fast convergence, high cooperative capability, and more reasonable planned trajectory sets when processing MUCTP. multi-UAV cooperative trajectory planning 3D feasible domain space adaptive differential evolutionary algorithm key trajectory points code correction method Motor vehicles. Aeronautics. Astronautics Min Hu verfasserin aut Xueying Yang verfasserin aut Peng Lin verfasserin aut In Drones MDPI AG, 2018 7(2023), 1, p 55 (DE-627)1025498356 2504446X nnns volume:7 year:2023 number:1, p 55 https://doi.org/10.3390/drones7010055 kostenfrei https://doaj.org/article/bef3341da91b4942ad97bbd0f7e07624 kostenfrei https://www.mdpi.com/2504-446X/7/1/55 kostenfrei https://doaj.org/toc/2504-446X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2023 1, p 55
allfieldsSound	10.3390/drones7010055 doi (DE-627)DOAJ081817290 (DE-599)DOAJbef3341da91b4942ad97bbd0f7e07624 DE-627 ger DE-627 rakwb eng TL1-4050 Gang Huang verfasserin aut Multi-UAV Cooperative Trajectory Planning Based on FDS-ADEA in Complex Environments 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-UAV cooperative trajectory planning (MUCTP) refers to the planning of multiple flyable trajectories based on the location of each UAV and mission point in a complex environment. In the planning process, the complex 3D space structure increases the difficulty of solving the trajectory points, and the mutual constraints of the UAV cooperative constraints can degrade the performance of the planning system. Therefore, to improve the efficiency of MUCTP, this study proposes MUCTP based on feasible domain space and adaptive differential evolution algorithm (FDS-ADEA). The method first constructs a three-dimensional feasible domain space to reduce the complexity of the search space structure; then, the constraints of heterogeneous UAVs are linearly weighted and transformed into a new objective function, and the information of the fitness value is shared in accordance with the adaptive method and the code correction method to improve the search efficiency of the algorithm; finally, the trajectories are smoothed to ensure the flyability of the UAVs performing the mission by combining the cubic B-spline curves. Experiments 1, 2, 3, and 4 validate the proposed algorithm. Simulation results verify that FDS-ADEA has fast convergence, high cooperative capability, and more reasonable planned trajectory sets when processing MUCTP. multi-UAV cooperative trajectory planning 3D feasible domain space adaptive differential evolutionary algorithm key trajectory points code correction method Motor vehicles. Aeronautics. Astronautics Min Hu verfasserin aut Xueying Yang verfasserin aut Peng Lin verfasserin aut In Drones MDPI AG, 2018 7(2023), 1, p 55 (DE-627)1025498356 2504446X nnns volume:7 year:2023 number:1, p 55 https://doi.org/10.3390/drones7010055 kostenfrei https://doaj.org/article/bef3341da91b4942ad97bbd0f7e07624 kostenfrei https://www.mdpi.com/2504-446X/7/1/55 kostenfrei https://doaj.org/toc/2504-446X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2023 1, p 55
language	English
source	In Drones 7(2023), 1, p 55 volume:7 year:2023 number:1, p 55
sourceStr	In Drones 7(2023), 1, p 55 volume:7 year:2023 number:1, p 55
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	multi-UAV cooperative trajectory planning 3D feasible domain space adaptive differential evolutionary algorithm key trajectory points code correction method Motor vehicles. Aeronautics. Astronautics
isfreeaccess_bool	true
container_title	Drones
authorswithroles_txt_mv	Gang Huang @@aut@@ Min Hu @@aut@@ Xueying Yang @@aut@@ Peng Lin @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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callnumber-first	T - Technology
author	Gang Huang
spellingShingle	Gang Huang misc TL1-4050 misc multi-UAV cooperative trajectory planning misc 3D feasible domain space misc adaptive differential evolutionary algorithm misc key trajectory points misc code correction method misc Motor vehicles. Aeronautics. Astronautics Multi-UAV Cooperative Trajectory Planning Based on FDS-ADEA in Complex Environments
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topic_title	TL1-4050 Multi-UAV Cooperative Trajectory Planning Based on FDS-ADEA in Complex Environments multi-UAV cooperative trajectory planning 3D feasible domain space adaptive differential evolutionary algorithm key trajectory points code correction method
topic	misc TL1-4050 misc multi-UAV cooperative trajectory planning misc 3D feasible domain space misc adaptive differential evolutionary algorithm misc key trajectory points misc code correction method misc Motor vehicles. Aeronautics. Astronautics
topic_unstemmed	misc TL1-4050 misc multi-UAV cooperative trajectory planning misc 3D feasible domain space misc adaptive differential evolutionary algorithm misc key trajectory points misc code correction method misc Motor vehicles. Aeronautics. Astronautics
topic_browse	misc TL1-4050 misc multi-UAV cooperative trajectory planning misc 3D feasible domain space misc adaptive differential evolutionary algorithm misc key trajectory points misc code correction method misc Motor vehicles. Aeronautics. Astronautics
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	Multi-UAV Cooperative Trajectory Planning Based on FDS-ADEA in Complex Environments
ctrlnum	(DE-627)DOAJ081817290 (DE-599)DOAJbef3341da91b4942ad97bbd0f7e07624
title_full	Multi-UAV Cooperative Trajectory Planning Based on FDS-ADEA in Complex Environments
author_sort	Gang Huang
journal	Drones
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title_sort	multi-uav cooperative trajectory planning based on fds-adea in complex environments
callnumber	TL1-4050
title_auth	Multi-UAV Cooperative Trajectory Planning Based on FDS-ADEA in Complex Environments
abstract	Multi-UAV cooperative trajectory planning (MUCTP) refers to the planning of multiple flyable trajectories based on the location of each UAV and mission point in a complex environment. In the planning process, the complex 3D space structure increases the difficulty of solving the trajectory points, and the mutual constraints of the UAV cooperative constraints can degrade the performance of the planning system. Therefore, to improve the efficiency of MUCTP, this study proposes MUCTP based on feasible domain space and adaptive differential evolution algorithm (FDS-ADEA). The method first constructs a three-dimensional feasible domain space to reduce the complexity of the search space structure; then, the constraints of heterogeneous UAVs are linearly weighted and transformed into a new objective function, and the information of the fitness value is shared in accordance with the adaptive method and the code correction method to improve the search efficiency of the algorithm; finally, the trajectories are smoothed to ensure the flyability of the UAVs performing the mission by combining the cubic B-spline curves. Experiments 1, 2, 3, and 4 validate the proposed algorithm. Simulation results verify that FDS-ADEA has fast convergence, high cooperative capability, and more reasonable planned trajectory sets when processing MUCTP.
abstractGer	Multi-UAV cooperative trajectory planning (MUCTP) refers to the planning of multiple flyable trajectories based on the location of each UAV and mission point in a complex environment. In the planning process, the complex 3D space structure increases the difficulty of solving the trajectory points, and the mutual constraints of the UAV cooperative constraints can degrade the performance of the planning system. Therefore, to improve the efficiency of MUCTP, this study proposes MUCTP based on feasible domain space and adaptive differential evolution algorithm (FDS-ADEA). The method first constructs a three-dimensional feasible domain space to reduce the complexity of the search space structure; then, the constraints of heterogeneous UAVs are linearly weighted and transformed into a new objective function, and the information of the fitness value is shared in accordance with the adaptive method and the code correction method to improve the search efficiency of the algorithm; finally, the trajectories are smoothed to ensure the flyability of the UAVs performing the mission by combining the cubic B-spline curves. Experiments 1, 2, 3, and 4 validate the proposed algorithm. Simulation results verify that FDS-ADEA has fast convergence, high cooperative capability, and more reasonable planned trajectory sets when processing MUCTP.
abstract_unstemmed	Multi-UAV cooperative trajectory planning (MUCTP) refers to the planning of multiple flyable trajectories based on the location of each UAV and mission point in a complex environment. In the planning process, the complex 3D space structure increases the difficulty of solving the trajectory points, and the mutual constraints of the UAV cooperative constraints can degrade the performance of the planning system. Therefore, to improve the efficiency of MUCTP, this study proposes MUCTP based on feasible domain space and adaptive differential evolution algorithm (FDS-ADEA). The method first constructs a three-dimensional feasible domain space to reduce the complexity of the search space structure; then, the constraints of heterogeneous UAVs are linearly weighted and transformed into a new objective function, and the information of the fitness value is shared in accordance with the adaptive method and the code correction method to improve the search efficiency of the algorithm; finally, the trajectories are smoothed to ensure the flyability of the UAVs performing the mission by combining the cubic B-spline curves. Experiments 1, 2, 3, and 4 validate the proposed algorithm. Simulation results verify that FDS-ADEA has fast convergence, high cooperative capability, and more reasonable planned trajectory sets when processing MUCTP.
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container_issue	1, p 55
title_short	Multi-UAV Cooperative Trajectory Planning Based on FDS-ADEA in Complex Environments
url	https://doi.org/10.3390/drones7010055 https://doaj.org/article/bef3341da91b4942ad97bbd0f7e07624 https://www.mdpi.com/2504-446X/7/1/55 https://doaj.org/toc/2504-446X
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doi_str	10.3390/drones7010055
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up_date	2024-07-03T22:07:18.241Z
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