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Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach

Autonomous mission capabilities with optimal path are stringent requirements for Unmanned Aerial Vehicle (UAV) navigation in diverse applications. The proposed research framework is to identify an energy-efficient optimal path to achieve the designated missions for the navigation of UAVs in various...

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Autores principales: Balasubramanian, E., Elangovan, E., Tamilarasan, P., Kanagachidambaresan, G. R., Chutia, Dibyajyoti
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9244350/
https://www.ncbi.nlm.nih.gov/pubmed/35789596
http://dx.doi.org/10.1007/s12652-022-04098-z
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author Balasubramanian, E.
Elangovan, E.
Tamilarasan, P.
Kanagachidambaresan, G. R.
Chutia, Dibyajyoti
author_facet Balasubramanian, E.
Elangovan, E.
Tamilarasan, P.
Kanagachidambaresan, G. R.
Chutia, Dibyajyoti
author_sort Balasubramanian, E.
collection PubMed
description Autonomous mission capabilities with optimal path are stringent requirements for Unmanned Aerial Vehicle (UAV) navigation in diverse applications. The proposed research framework is to identify an energy-efficient optimal path to achieve the designated missions for the navigation of UAVs in various constrained and denser obstacle prone regions. Hence, the present work is aimed to develop an optimal energy-efficient path planning algorithm through combining well known modified ant colony optimization algorithm (MACO) and a variant of A*, namely the memory-efficient A* algorithm (MEA*) for avoiding the obstacles in three dimensional (3D) environment and arrive at an optimal path with minimal energy consumption. The novelty of the proposed method relies on integrating the above two efficient algorithms to optimize the UAV path planning task. The basic design of this study is, that by utilizing an improved version of the pheromone strategy in MACO, the local trap and premature convergence are minimized, and also an optimal path is found by means of reward and penalty mechanism. The sole notion of integrating the MEA* algorithm arises from the fact that it is essential to overcome the stringent memory requirement of conventional A* algorithm and to resolve the issue of tracking only the edges of the grids. Combining the competencies of MACO and MEA*, a hybrid algorithm is proposed to avoid obstacles and find an efficient path. Simulation studies are performed by varying the number of obstacles in a 3D domain. The real-time flight trials are conducted experimentally using a UAV by implementing the attained optimal path. A comparison of the total energy consumption of UAV with theoretical analysis is accomplished. The significant finding of this study is that, the MACO-MEA* algorithm achieved 21% less energy consumption and 55% shorter execution time than the MACO-A*. moreover, the path traversed in both simulation and experimental methods is 99% coherent with each other. it confirms that the developed hybrid MACO-MEA* energy-efficient algorithm is a viable solution for UAV navigation in 3D obstacles prone regions.
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spelling pubmed-92443502022-06-30 Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach Balasubramanian, E. Elangovan, E. Tamilarasan, P. Kanagachidambaresan, G. R. Chutia, Dibyajyoti J Ambient Intell Humaniz Comput Original Research Autonomous mission capabilities with optimal path are stringent requirements for Unmanned Aerial Vehicle (UAV) navigation in diverse applications. The proposed research framework is to identify an energy-efficient optimal path to achieve the designated missions for the navigation of UAVs in various constrained and denser obstacle prone regions. Hence, the present work is aimed to develop an optimal energy-efficient path planning algorithm through combining well known modified ant colony optimization algorithm (MACO) and a variant of A*, namely the memory-efficient A* algorithm (MEA*) for avoiding the obstacles in three dimensional (3D) environment and arrive at an optimal path with minimal energy consumption. The novelty of the proposed method relies on integrating the above two efficient algorithms to optimize the UAV path planning task. The basic design of this study is, that by utilizing an improved version of the pheromone strategy in MACO, the local trap and premature convergence are minimized, and also an optimal path is found by means of reward and penalty mechanism. The sole notion of integrating the MEA* algorithm arises from the fact that it is essential to overcome the stringent memory requirement of conventional A* algorithm and to resolve the issue of tracking only the edges of the grids. Combining the competencies of MACO and MEA*, a hybrid algorithm is proposed to avoid obstacles and find an efficient path. Simulation studies are performed by varying the number of obstacles in a 3D domain. The real-time flight trials are conducted experimentally using a UAV by implementing the attained optimal path. A comparison of the total energy consumption of UAV with theoretical analysis is accomplished. The significant finding of this study is that, the MACO-MEA* algorithm achieved 21% less energy consumption and 55% shorter execution time than the MACO-A*. moreover, the path traversed in both simulation and experimental methods is 99% coherent with each other. it confirms that the developed hybrid MACO-MEA* energy-efficient algorithm is a viable solution for UAV navigation in 3D obstacles prone regions. Springer Berlin Heidelberg 2022-06-25 /pmc/articles/PMC9244350/ /pubmed/35789596 http://dx.doi.org/10.1007/s12652-022-04098-z Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Research
Balasubramanian, E.
Elangovan, E.
Tamilarasan, P.
Kanagachidambaresan, G. R.
Chutia, Dibyajyoti
Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach
title Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach
title_full Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach
title_fullStr Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach
title_full_unstemmed Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach
title_short Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach
title_sort optimal energy efficient path planning of uav using hybrid maco-mea* algorithm: theoretical and experimental approach
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9244350/
https://www.ncbi.nlm.nih.gov/pubmed/35789596
http://dx.doi.org/10.1007/s12652-022-04098-z
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