An Effective Way for Repositioning the Beacon Nodes of Fast RRT Results Utilizing Grey Wolf Optimization
Conceptually, Fast-RRT applies fast sampling and random steering which makes the initial path quickly obtained. Referring to the initial path, the optimality of the path is improved by applying path fusion and path optimization. Theoretically, path fusion will only be optimal if there is always a un...
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| Language: | en |
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2025
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| Online Access: | http://eprints.uthm.edu.my/12794/1/J19448_edf4eec7c27c70a315e32fe81b8bd016.pdf http://eprints.uthm.edu.my/12794/ https://doi.org/10.18196/jrc.v6i1.22062 |
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| author | Suwoyo, Heru Adriansyah, Andi Andika, Julpri Shamsudin, Abu Ubaidah Tian, Yingzhong |
| author_facet | Suwoyo, Heru Adriansyah, Andi Andika, Julpri Shamsudin, Abu Ubaidah Tian, Yingzhong |
| author_sort | Suwoyo, Heru |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Conceptually, Fast-RRT applies fast sampling and random steering which makes the initial path quickly obtained. Referring to the initial path, the optimality of the path is improved by applying path fusion and path optimization. Theoretically, path fusion will only be optimal if there is always a unique/different path to be fused with the previously obtained path. However, in the conditions of solving path planning problems in narrow corridors, the potential for obtaining a
different path from the previous one is very small. So that fusion does not run properly, but checking the relationship between nodes to nodes still occurs. Instead of getting an optimal path in conditions like this, the computation will increase, the solution time will be long, and the resulting path will still be sub-optimal. As an effort to solve this problem, Grey Wolf Optimization (GWO) is involved through this study. While an initial path is found, the beacons are repositioned. From the path, the number of nodes is unpredictable, causing the decision variables in optimization to become large. For this reason, the GWO is
chosen because it is independent of population representation and is not affected by the number of decision variables. This proposed method is claimed to be more effective in solving path planning problems in terms of convergence rate and optimality. Therefore, the proposed method is evaluated and compared
with previous methods and gives the result that the average working speed of Fast-RRT is improved by 90.25% and the optimality average increased by 5.67%. |
| format | Article |
| id | my.uthm.eprints-12794 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my.uthm.eprints-127942025-07-01T23:52:18Z http://eprints.uthm.edu.my/12794/ An Effective Way for Repositioning the Beacon Nodes of Fast RRT Results Utilizing Grey Wolf Optimization Suwoyo, Heru Adriansyah, Andi Andika, Julpri Shamsudin, Abu Ubaidah Tian, Yingzhong TJ Mechanical engineering and machinery Conceptually, Fast-RRT applies fast sampling and random steering which makes the initial path quickly obtained. Referring to the initial path, the optimality of the path is improved by applying path fusion and path optimization. Theoretically, path fusion will only be optimal if there is always a unique/different path to be fused with the previously obtained path. However, in the conditions of solving path planning problems in narrow corridors, the potential for obtaining a different path from the previous one is very small. So that fusion does not run properly, but checking the relationship between nodes to nodes still occurs. Instead of getting an optimal path in conditions like this, the computation will increase, the solution time will be long, and the resulting path will still be sub-optimal. As an effort to solve this problem, Grey Wolf Optimization (GWO) is involved through this study. While an initial path is found, the beacons are repositioned. From the path, the number of nodes is unpredictable, causing the decision variables in optimization to become large. For this reason, the GWO is chosen because it is independent of population representation and is not affected by the number of decision variables. This proposed method is claimed to be more effective in solving path planning problems in terms of convergence rate and optimality. Therefore, the proposed method is evaluated and compared with previous methods and gives the result that the average working speed of Fast-RRT is improved by 90.25% and the optimality average increased by 5.67%. 2025 Article PeerReviewed text en http://eprints.uthm.edu.my/12794/1/J19448_edf4eec7c27c70a315e32fe81b8bd016.pdf Suwoyo, Heru and Adriansyah, Andi and Andika, Julpri and Shamsudin, Abu Ubaidah and Tian, Yingzhong (2025) An Effective Way for Repositioning the Beacon Nodes of Fast RRT Results Utilizing Grey Wolf Optimization. Journal of Robotics and Control (JRC), 6 (1). pp. 272-284. ISSN 2715-5072 https://doi.org/10.18196/jrc.v6i1.22062 |
| spellingShingle | TJ Mechanical engineering and machinery Suwoyo, Heru Adriansyah, Andi Andika, Julpri Shamsudin, Abu Ubaidah Tian, Yingzhong An Effective Way for Repositioning the Beacon Nodes of Fast RRT Results Utilizing Grey Wolf Optimization |
| title | An Effective Way for Repositioning the Beacon Nodes of Fast RRT Results Utilizing Grey Wolf Optimization |
| title_full | An Effective Way for Repositioning the Beacon Nodes of Fast RRT Results Utilizing Grey Wolf Optimization |
| title_fullStr | An Effective Way for Repositioning the Beacon Nodes of Fast RRT Results Utilizing Grey Wolf Optimization |
| title_full_unstemmed | An Effective Way for Repositioning the Beacon Nodes of Fast RRT Results Utilizing Grey Wolf Optimization |
| title_short | An Effective Way for Repositioning the Beacon Nodes of Fast RRT Results Utilizing Grey Wolf Optimization |
| title_sort | effective way for repositioning the beacon nodes of fast rrt results utilizing grey wolf optimization |
| topic | TJ Mechanical engineering and machinery |
| url | http://eprints.uthm.edu.my/12794/1/J19448_edf4eec7c27c70a315e32fe81b8bd016.pdf http://eprints.uthm.edu.my/12794/ https://doi.org/10.18196/jrc.v6i1.22062 |
| url_provider | http://eprints.uthm.edu.my/ |