Tide-Inspired Path Planning Algorithm for Autonomous Vehicles

With the extensive developments in autonomous vehicles (AV) and the increase of interest in artificial intelligence (AI), path planning is becoming a focal area of research. However, path planning is an NP-hard problem and its execution time and complexity are major concerns when searching for optim...

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Autores principales:	Heba Kurdi, Shaden Almuhalhel, Hebah Elgibreen, Hajar Qahmash, Bayan Albatati, Lubna Al-Salem, Ghada Almoaiqel
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	path planning nature-inspired algorithms autonomous vehicles transportation Science Q
Acceso en línea:	https://doaj.org/article/bb742d771005433da6ca25fb3a276992
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id	oai:doaj.org-article:bb742d771005433da6ca25fb3a276992
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spelling	oai:doaj.org-article:bb742d771005433da6ca25fb3a2769922021-11-25T18:55:05ZTide-Inspired Path Planning Algorithm for Autonomous Vehicles10.3390/rs132246442072-4292https://doaj.org/article/bb742d771005433da6ca25fb3a2769922021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4644https://doaj.org/toc/2072-4292With the extensive developments in autonomous vehicles (AV) and the increase of interest in artificial intelligence (AI), path planning is becoming a focal area of research. However, path planning is an NP-hard problem and its execution time and complexity are major concerns when searching for optimal solutions. Thus, the optimal trade-off between the shortest path and computing resources must be found. This paper introduces a path planning algorithm, tide path planning (TPP), which is inspired by the natural tide phenomenon. The idea of the gravitational attraction between the Earth and the Moon is adopted to avoid searching blocked routes and to find a shortest path. Benchmarking the performance of the proposed algorithm against rival path planning algorithms, such as A*, breadth-first search (BFS), Dijkstra, and genetic algorithms (GA), revealed that the proposed TPP algorithm succeeded in finding a shortest path while visiting the least number of cells and showed the fastest execution time under different settings of environment size and obstacle ratios.Heba KurdiShaden AlmuhalhelHebah ElgibreenHajar QahmashBayan AlbatatiLubna Al-SalemGhada AlmoaiqelMDPI AGarticlepath planningnature-inspired algorithmsautonomous vehiclestransportationScienceQENRemote Sensing, Vol 13, Iss 4644, p 4644 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	path planning nature-inspired algorithms autonomous vehicles transportation Science Q
spellingShingle	path planning nature-inspired algorithms autonomous vehicles transportation Science Q Heba Kurdi Shaden Almuhalhel Hebah Elgibreen Hajar Qahmash Bayan Albatati Lubna Al-Salem Ghada Almoaiqel Tide-Inspired Path Planning Algorithm for Autonomous Vehicles
description	With the extensive developments in autonomous vehicles (AV) and the increase of interest in artificial intelligence (AI), path planning is becoming a focal area of research. However, path planning is an NP-hard problem and its execution time and complexity are major concerns when searching for optimal solutions. Thus, the optimal trade-off between the shortest path and computing resources must be found. This paper introduces a path planning algorithm, tide path planning (TPP), which is inspired by the natural tide phenomenon. The idea of the gravitational attraction between the Earth and the Moon is adopted to avoid searching blocked routes and to find a shortest path. Benchmarking the performance of the proposed algorithm against rival path planning algorithms, such as A*, breadth-first search (BFS), Dijkstra, and genetic algorithms (GA), revealed that the proposed TPP algorithm succeeded in finding a shortest path while visiting the least number of cells and showed the fastest execution time under different settings of environment size and obstacle ratios.
format	article
author	Heba Kurdi Shaden Almuhalhel Hebah Elgibreen Hajar Qahmash Bayan Albatati Lubna Al-Salem Ghada Almoaiqel
author_facet	Heba Kurdi Shaden Almuhalhel Hebah Elgibreen Hajar Qahmash Bayan Albatati Lubna Al-Salem Ghada Almoaiqel
author_sort	Heba Kurdi
title	Tide-Inspired Path Planning Algorithm for Autonomous Vehicles
title_short	Tide-Inspired Path Planning Algorithm for Autonomous Vehicles
title_full	Tide-Inspired Path Planning Algorithm for Autonomous Vehicles
title_fullStr	Tide-Inspired Path Planning Algorithm for Autonomous Vehicles
title_full_unstemmed	Tide-Inspired Path Planning Algorithm for Autonomous Vehicles
title_sort	tide-inspired path planning algorithm for autonomous vehicles
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bb742d771005433da6ca25fb3a276992
work_keys_str_mv	AT hebakurdi tideinspiredpathplanningalgorithmforautonomousvehicles AT shadenalmuhalhel tideinspiredpathplanningalgorithmforautonomousvehicles AT hebahelgibreen tideinspiredpathplanningalgorithmforautonomousvehicles AT hajarqahmash tideinspiredpathplanningalgorithmforautonomousvehicles AT bayanalbatati tideinspiredpathplanningalgorithmforautonomousvehicles AT lubnaalsalem tideinspiredpathplanningalgorithmforautonomousvehicles AT ghadaalmoaiqel tideinspiredpathplanningalgorithmforautonomousvehicles
_version_	1718410549026357248

Tide-Inspired Path Planning Algorithm for Autonomous Vehicles

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