Optimasi Performansi Pengendalian Robot Swarm menggunakan Logika Fuzzy Tipe 2-Particle Swarm Optimazation

  • Gita Fadila Fitriana Institut Teknologi Telkom Purwokerto
Keywords: swarm robot, leader follower approach, type 2 fuzzy logic, particle swarm optimization


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.



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L. Hakim and V. Yonatan, “Deteksi Kebocoran Gas LPG menggunakan Detektor Arduino dengan algoritma Fuzzy Logic Mandani,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 2, p. 114, 2017, doi: 10.29207/resti.v1i2.35.

I. Ikhsan and A. A. Putra, “Autonomous Sales Robot untuk Pengenal Produk Berbasis Barcode dan Arduino ATMega328,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 2, no. 1, pp. 397–402, 2018, doi: 10.29207/resti.v2i1.264.

G. Li and W. Chou, “Path planning for mobile robot using self-adaptive learning particle swarm optimization,” Sci. China Inf. Sci., vol. 61, no. 5, p. 52204, 2017, doi: 10.1007/s11432-016-9115-2.

W. O. Quesada et al., “Leader-Follower Formation for UAV Robot Swarm Based on Fuzzy Logic Theory,” in Artificial Intelligence and Soft Computing, 2018, pp. 740–751.

W. He and Y. Dong, “Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning,” IEEE Trans. Neural Networks Learn. Syst., vol. 29, no. 4, pp. 1174–1186, 2018, doi: 10.1109/TNNLS.2017.2665581.

W. Sun, S.-F. Su, J. Xia, and V.-T. Nguyen, “Adaptive Fuzzy Tracking Control of Flexible-Joint Robots With Full-State Constraints,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 49, no. 11, pp. 2201–2209, 2019, doi: 10.1109/TSMC.2018.2870642.

T. Yifei, Z. Meng, L. Jingwei, L. Dongbo, and W. Yulin, “Research on Intelligent Welding Robot Path Optimization Based on GA and PSO Algorithms,” IEEE Access, vol. 6, pp. 65397–65404, 2018, doi: 10.1109/ACCESS.2018.2878615.

D. M. B. Tarigan, D. P. Rini, and Samsuryadi, “Seleksi Fitur pada Klasifikasi Penyakit Gula Darah Menggunakan Particle Swarm Optimization (PSO) pada Algoritma C4.5,” vol. 4, no. 3, pp. 569–575, 2020.

Ipriadi, G. W. Nurcahyo, and J. Santony, “Pendeteksi Volume Air Pendeteksi Volume Air Secara Otomatis Menggunakan Fuzzy,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 1, pp. 11–16, 2019, doi: 10.29207/resti.v3i1.738.

B. K. Patle, G. Babu L, A. Pandey, D. R. K. Parhi, and A. Jagadeesh, “A review: On path planning strategies for navigation of mobile robot,” Def. Technol., vol. 15, no. 4, pp. 582–606, 2019, doi: https://doi.org/10.1016/j.dt.2019.04.011.

Z. Li, Y. Yuan, F. Ke, W. He, and C.-Y. Su, “Robust Vision-Based Tube Model Predictive Control of Multiple Mobile Robots for Leader–Follower Formation,” IEEE Trans. Ind. Electron., vol. 67, no. 4, pp. 3096–3106, 2020, doi: 10.1109/TIE.2019.2913813.

J. Campos, S. Jaramillo, L. Morales, O. Camacho, D. Chávez, and D. Pozo, “PSO Tuning for Fuzzy PD + I Controller Applied to a Mobile Robot Trajectory Control,” in 2018 International Conference on Information Systems and Computer Science (INCISCOS), 2018, pp. 62–68, doi: 10.1109/INCISCOS.2018.00017.

K. Putriyani, T. Wahyuningrum, and Y. D. Prasetyo, “Prediksi Jumlah Produksi Akibat Penyebaran Covid-19 Menggunakan Metode Fuzzy Takagi-Sugeno,” vol. 1, no. 10, pp. 220–230, 2021.

B. AL-Madani, F. Orujov, R. Maskeliūnas, R. Damaševičius, and A. Venčkauskas, “Fuzzy Logic Type-2 Based Wireless Indoor Localization System for Navigation of Visually Impaired People in Buildings,” Sensors, vol. 19, no. 9, 2019, doi: 10.3390/s19092114.

K. Hamdi, “Analisis Data Sistem Informasi Geografis Rumah Tidak Layak Huni (RTLH) menggunakan Metode Fuzzy Logic,” vol. 1, no. 1, pp. 19–25, 2017.

Fitriana, Gita Fadila and R. Adhitama, “Performansi Navigasi Robot Leader-Follower menggunakan Algoritma Logika Fuzzy Interval Tipe 2,” Rekayasa Sist. dan Teknol. Inf., vol. 3, no. 3, pp. 371–376, 2019.

S. D. Anggita and Ikmah, “Komparasi Algoritma Klasifikasi berbasis Particle Swarm Optimization pada Analisis Sentimen Ekspedisi Barang,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 2, pp. 362–369, 2020, doi: 10.29207/resti.v4i2.1840.

N. Hafidz and D. Y. Liliana, “Klasifikasi Sentimen pada Twitter Terhadap WHO Terkait Covid-19 menggunakan SVM, N-Gram, PSO,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 10, pp. 3–4, 2021.

I. Romli, F. Kharida, and C. Naya, “Penentuan Kepuasan Pelanggan Terhadap Pelayanan Menggunakan C4.5 dan PSO,” vol. 1, no. 10, 2021.

M. Bakhale, V. Hemalatha, S. Dhanalakshmi, R. Kumar, and M. Siddharth Jain, “A Dynamic Inertial Weight Strategy in Micro PSO for Swarm Robots,” Wirel. Pers. Commun., vol. 110, no. 2, pp. 573–592, 2020, doi: 10.1007/s11277-019-06743-x.

How to Cite
Fitriana, G. F. (2021). Optimasi Performansi Pengendalian Robot Swarm menggunakan Logika Fuzzy Tipe 2-Particle Swarm Optimazation . Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(3), 602 - 608. https://doi.org/10.29207/resti.v5i3.3194
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