Optimization of Fuzzy Social Force Model Adaptive Parameter using Genetic Algorithm for Mobile Robot Navigation Control
The Social Force Model (SFM) is a popular navigation technique for mobile robots that is primarily used to simulate pedestrian movement. The SFM method's drawback is that several parameter values, such as gain, k, and impact range, σ, must be determined manually. The reaction of the SFM is freq...
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| Format: | Article |
| Language: | English |
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Universitas Syiah Kuala
2023-03-01
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| Series: | Jurnal Rekayasa Elektrika |
| Subjects: | |
| Online Access: | https://jurnal.usk.ac.id/JRE/article/view/28330 |
| _version_ | 1797742146985394176 |
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| author | Alif Wicaksana Ramadhan Bima Sena Bayu Dewantara Setiawardhana Setiawardhana |
| author_facet | Alif Wicaksana Ramadhan Bima Sena Bayu Dewantara Setiawardhana Setiawardhana |
| author_sort | Alif Wicaksana Ramadhan |
| collection | DOAJ |
| description | The Social Force Model (SFM) is a popular navigation technique for mobile robots that is primarily used to simulate pedestrian movement. The SFM method's drawback is that several parameter values, such as gain, k, and impact range, σ, must be determined manually. The reaction of the SFM is frequently inappropriate for certain environmental circumstances as a result of this manual determination. In this paper, we propose employing the Fuzzy Inference System (FIS), whose rules are optimized using a Genetic Algorithm (GA) to manage the value of the gain, k, parameter adaptive. The relative distance, d, and relative angle, α, concerning the robot's obstacle are the inputs for the FIS. The test results using a 3-D realistic CoppeliaSim demonstrated that the learning outcomes of FIS rules could provide adaptive parameter values suitable for each environmental circumstance, allowing the robot to travel smoothly is represented using the robot’s heading deviation which decreasing by and reaching the goal 1.6 sec faster from the starting point to the goal, compared to the SFM with the fixed parameter value. So that the proposed method is more effective and promising when deploying on the real robot implementation. |
| first_indexed | 2024-03-12T14:36:48Z |
| format | Article |
| id | doaj.art-04fe22d2b1c54f069060524c905f047e |
| institution | Directory Open Access Journal |
| issn | 1412-4785 2252-620X |
| language | English |
| last_indexed | 2024-03-12T14:36:48Z |
| publishDate | 2023-03-01 |
| publisher | Universitas Syiah Kuala |
| record_format | Article |
| series | Jurnal Rekayasa Elektrika |
| spelling | doaj.art-04fe22d2b1c54f069060524c905f047e2023-08-17T03:20:59ZengUniversitas Syiah KualaJurnal Rekayasa Elektrika1412-47852252-620X2023-03-0119110.17529/jre.v19i1.2833015742Optimization of Fuzzy Social Force Model Adaptive Parameter using Genetic Algorithm for Mobile Robot Navigation ControlAlif Wicaksana Ramadhan0Bima Sena Bayu Dewantara1Setiawardhana Setiawardhana2Politeknik Elektronika Negeri SurabayaPoliteknik Elektronika Negeri SurabayaPoliteknik Elektronika Negeri SurabayaThe Social Force Model (SFM) is a popular navigation technique for mobile robots that is primarily used to simulate pedestrian movement. The SFM method's drawback is that several parameter values, such as gain, k, and impact range, σ, must be determined manually. The reaction of the SFM is frequently inappropriate for certain environmental circumstances as a result of this manual determination. In this paper, we propose employing the Fuzzy Inference System (FIS), whose rules are optimized using a Genetic Algorithm (GA) to manage the value of the gain, k, parameter adaptive. The relative distance, d, and relative angle, α, concerning the robot's obstacle are the inputs for the FIS. The test results using a 3-D realistic CoppeliaSim demonstrated that the learning outcomes of FIS rules could provide adaptive parameter values suitable for each environmental circumstance, allowing the robot to travel smoothly is represented using the robot’s heading deviation which decreasing by and reaching the goal 1.6 sec faster from the starting point to the goal, compared to the SFM with the fixed parameter value. So that the proposed method is more effective and promising when deploying on the real robot implementation.https://jurnal.usk.ac.id/JRE/article/view/28330social force modelgainimpact rangefuzzy inference systemgenetic algorithm |
| spellingShingle | Alif Wicaksana Ramadhan Bima Sena Bayu Dewantara Setiawardhana Setiawardhana Optimization of Fuzzy Social Force Model Adaptive Parameter using Genetic Algorithm for Mobile Robot Navigation Control Jurnal Rekayasa Elektrika social force model gain impact range fuzzy inference system genetic algorithm |
| title | Optimization of Fuzzy Social Force Model Adaptive Parameter using Genetic Algorithm for Mobile Robot Navigation Control |
| title_full | Optimization of Fuzzy Social Force Model Adaptive Parameter using Genetic Algorithm for Mobile Robot Navigation Control |
| title_fullStr | Optimization of Fuzzy Social Force Model Adaptive Parameter using Genetic Algorithm for Mobile Robot Navigation Control |
| title_full_unstemmed | Optimization of Fuzzy Social Force Model Adaptive Parameter using Genetic Algorithm for Mobile Robot Navigation Control |
| title_short | Optimization of Fuzzy Social Force Model Adaptive Parameter using Genetic Algorithm for Mobile Robot Navigation Control |
| title_sort | optimization of fuzzy social force model adaptive parameter using genetic algorithm for mobile robot navigation control |
| topic | social force model gain impact range fuzzy inference system genetic algorithm |
| url | https://jurnal.usk.ac.id/JRE/article/view/28330 |
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