Optimasi Performansi Pengendalian Robot Swarm menggunakan Logika Fuzzy Tipe 2-Particle Swarm Optimazation
Robot control is currently very helpful for human work to be more effective and efficient both in completion time and in mitigating the risk of work accidents that may occur. This study determines the direction of the robot so that it does not collide with each other and reach the target. Controllin...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2021-06-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/3194 |
| _version_ | 1797334008584994816 |
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| author | Gita Fadila Fitriana |
| author_facet | Gita Fadila Fitriana |
| author_sort | Gita Fadila Fitriana |
| collection | DOAJ |
| description | Robot control is currently very helpful for human work to be more effective and efficient both in completion time and in mitigating the risk of work accidents that may occur. This study determines the direction of the robot so that it does not collide with each other and reach the target. Controlling the swarm robot with the leader-follower approach uses Fuzzy Logic Type 2-Particle Swarm Optimization (PSO) to optimise the performance of the swarm robot. The Fuzzy Logic Method Type 2 measures the direction decisions of the leader robot and follower robot using a rule base of 8 rules; the leader-follower robot is given a target. Achieving targets using PSO, the PSO process looks for potential solutions with quality references to reach the target as the optimal solution. The leader-follower modelling has been modelled using kinematic equations and controlling the movement of the robot's trajectory in the form of a simulation that has been carried out. The measurement results based on robot data in an open environment are 110 data, and a square environment is 1342. The measurement results based on robot time in a four-obstacle environment have the fastest time of 10.83 seconds and the longest time environment in an oval environment of 134.9 seconds. The measurement results are based on resources in a free environment of 10.6 kb and a square environment of 49.1 kb. Fuzzy Logic Type 2-PSO has a higher time indicating a stable speed result and judging from the trajectory in avoiding obstacles, and the leader-follower robot has a faster response. |
| first_indexed | 2024-03-08T08:13:34Z |
| format | Article |
| id | doaj.art-83eaaee7e82148ed952ba9d7d1908216 |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T08:13:34Z |
| publishDate | 2021-06-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-83eaaee7e82148ed952ba9d7d19082162024-02-02T07:59:28ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602021-06-015360260810.29207/resti.v5i3.31943194Optimasi Performansi Pengendalian Robot Swarm menggunakan Logika Fuzzy Tipe 2-Particle Swarm OptimazationGita Fadila Fitriana0Institut Teknologi Telkom PurwokertoRobot control is currently very helpful for human work to be more effective and efficient both in completion time and in mitigating the risk of work accidents that may occur. This study determines the direction of the robot so that it does not collide with each other and reach the target. Controlling the swarm robot with the leader-follower approach uses Fuzzy Logic Type 2-Particle Swarm Optimization (PSO) to optimise the performance of the swarm robot. The Fuzzy Logic Method Type 2 measures the direction decisions of the leader robot and follower robot using a rule base of 8 rules; the leader-follower robot is given a target. Achieving targets using PSO, the PSO process looks for potential solutions with quality references to reach the target as the optimal solution. The leader-follower modelling has been modelled using kinematic equations and controlling the movement of the robot's trajectory in the form of a simulation that has been carried out. The measurement results based on robot data in an open environment are 110 data, and a square environment is 1342. The measurement results based on robot time in a four-obstacle environment have the fastest time of 10.83 seconds and the longest time environment in an oval environment of 134.9 seconds. The measurement results are based on resources in a free environment of 10.6 kb and a square environment of 49.1 kb. Fuzzy Logic Type 2-PSO has a higher time indicating a stable speed result and judging from the trajectory in avoiding obstacles, and the leader-follower robot has a faster response.http://jurnal.iaii.or.id/index.php/RESTI/article/view/3194swarm robotleader follower approachtype 2 fuzzy logicparticle swarm optimization |
| spellingShingle | Gita Fadila Fitriana Optimasi Performansi Pengendalian Robot Swarm menggunakan Logika Fuzzy Tipe 2-Particle Swarm Optimazation Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) swarm robot leader follower approach type 2 fuzzy logic particle swarm optimization |
| title | Optimasi Performansi Pengendalian Robot Swarm menggunakan Logika Fuzzy Tipe 2-Particle Swarm Optimazation |
| title_full | Optimasi Performansi Pengendalian Robot Swarm menggunakan Logika Fuzzy Tipe 2-Particle Swarm Optimazation |
| title_fullStr | Optimasi Performansi Pengendalian Robot Swarm menggunakan Logika Fuzzy Tipe 2-Particle Swarm Optimazation |
| title_full_unstemmed | Optimasi Performansi Pengendalian Robot Swarm menggunakan Logika Fuzzy Tipe 2-Particle Swarm Optimazation |
| title_short | Optimasi Performansi Pengendalian Robot Swarm menggunakan Logika Fuzzy Tipe 2-Particle Swarm Optimazation |
| title_sort | optimasi performansi pengendalian robot swarm menggunakan logika fuzzy tipe 2 particle swarm optimazation |
| topic | swarm robot leader follower approach type 2 fuzzy logic particle swarm optimization |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/3194 |
| work_keys_str_mv | AT gitafadilafitriana optimasiperformansipengendalianrobotswarmmenggunakanlogikafuzzytipe2particleswarmoptimazation |