Intelligent Rapidly-Exploring Random Tree Star Algorithm
A Master of Science thesis in Mechatronics Engineering by Khidir Galal Eldin Khidir Ahmed entitled, “Intelligent Rapidly-Exploring Random Tree Star Algorithm”, submitted in May 2024. Thesis advisor is Dr. Mohammad Jaradat and thesis co-advisor is Dr. Lotfi Romdhane. Soft copy is available (Thesis, C...
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| Format: | doctoralThesis |
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2024
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| Online Access: | https://hdl.handle.net/11073/25629 |
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| _version_ | 1864513441110163456 |
|---|---|
| author | Ahmed, Khidir Galal Eldin Khidir |
| author_facet | Ahmed, Khidir Galal Eldin Khidir |
| author_role | author |
| dc.contributor.none.fl_str_mv | Jaradat, Mohammad Romdhane, Lotfi |
| dc.creator.none.fl_str_mv | Ahmed, Khidir Galal Eldin Khidir |
| dc.date.none.fl_str_mv | 2024-09-26T06:20:03Z 2024-09-26T06:20:03Z 2024-05 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 35.232-2024.36 https://hdl.handle.net/11073/25629 |
| dc.language.none.fl_str_mv | en_US |
| dc.subject.none.fl_str_mv | NA-RRT*N RRT* RRT*N Navigation Optimal path Mobile robot |
| dc.title.none.fl_str_mv | Intelligent Rapidly-Exploring Random Tree Star Algorithm |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/doctoralThesis |
| description | A Master of Science thesis in Mechatronics Engineering by Khidir Galal Eldin Khidir Ahmed entitled, “Intelligent Rapidly-Exploring Random Tree Star Algorithm”, submitted in May 2024. Thesis advisor is Dr. Mohammad Jaradat and thesis co-advisor is Dr. Lotfi Romdhane. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form). |
| format | doctoralThesis |
| id | aus_81dc5f8a1ec7580bd064bf72f2719255 |
| identifier_str_mv | 35.232-2024.36 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/25629 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Intelligent Rapidly-Exploring Random Tree Star AlgorithmAhmed, Khidir Galal Eldin KhidirNA-RRT*NRRT*RRT*NNavigationOptimal pathMobile robotA Master of Science thesis in Mechatronics Engineering by Khidir Galal Eldin Khidir Ahmed entitled, “Intelligent Rapidly-Exploring Random Tree Star Algorithm”, submitted in May 2024. Thesis advisor is Dr. Mohammad Jaradat and thesis co-advisor is Dr. Lotfi Romdhane. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Autonomous robots have been increasingly employed to supplant human labor across diverse fields over recent decades, serving as a foundational element in numerous industries ranging from supply chains and assembly lines to transportation. In these sectors, rapid and efficient operation is indispensable. Therefore, the development of advanced path planning techniques implies pivotal importance to mitigate human dependency. Hence, in this work, we developed an improved path planning algorithm inspired by the directional implementation in Rapidly-Exploring Random Tree Star Normal (RRT*N) and its variants, which is used to address the lack of environment adaptability and the improvement of path quality and inadequate long processing times. This new method is called Neural Adaptive Rapidly-Exploring Random Tree Star Normal (NA-RRT*N). The advanced presented method can deal with path planning problems in 2D and 3D environments. This novel method uses a Gaussian probability distribution with variable standard deviation to generate new nodes, which is controlled via Artificial Neural Network based on the environmental feedback. This feature results in a varied tree concentration in the direction of the target. It is shown that this method can be more than 68% faster in finding the initial path to the target and produces at least 5% shorter path in worst case scenario compared to three states of the art versions of RRT method. Furthermore, NA-RRT*N stood out with a perfect 100% success rate in all seven 2D scenarios tests while continually improving path smoothness. For instance, in 100 trials of the presented static scenarios, NA-RRT*N exhibited the shortest average processing time and path length across seven varied complexity maps.College of EngineeringMultidisciplinary ProgramsMaster of Science in Mechatronics Engineering (MSMTR)Jaradat, MohammadRomdhane, Lotfi2024-09-26T06:20:03Z2024-09-26T06:20:03Z2024-05info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2024.36https://hdl.handle.net/11073/25629en_USoai:repository.aus.edu:11073/256292025-06-26T12:23:47Z |
| spellingShingle | Intelligent Rapidly-Exploring Random Tree Star Algorithm Ahmed, Khidir Galal Eldin Khidir NA-RRT*N RRT* RRT*N Navigation Optimal path Mobile robot |
| status_str | publishedVersion |
| title | Intelligent Rapidly-Exploring Random Tree Star Algorithm |
| title_full | Intelligent Rapidly-Exploring Random Tree Star Algorithm |
| title_fullStr | Intelligent Rapidly-Exploring Random Tree Star Algorithm |
| title_full_unstemmed | Intelligent Rapidly-Exploring Random Tree Star Algorithm |
| title_short | Intelligent Rapidly-Exploring Random Tree Star Algorithm |
| title_sort | Intelligent Rapidly-Exploring Random Tree Star Algorithm |
| topic | NA-RRT*N RRT* RRT*N Navigation Optimal path Mobile robot |
| url | https://hdl.handle.net/11073/25629 |