Version 1
: Received: 26 August 2024 / Approved: 26 August 2024 / Online: 26 August 2024 (11:34:35 CEST)
How to cite:
Sulistijono, I. A.; Yuniawan, A.; Barakbah, A. R.; Arief, Z.; Kubota, N. Holonomic Mobile Robot Navigation System Based on Collaboration Fuzzy Controller and Particle Swarm Optimization. Preprints2024, 2024081815. https://doi.org/10.20944/preprints202408.1815.v1
Sulistijono, I. A.; Yuniawan, A.; Barakbah, A. R.; Arief, Z.; Kubota, N. Holonomic Mobile Robot Navigation System Based on Collaboration Fuzzy Controller and Particle Swarm Optimization. Preprints 2024, 2024081815. https://doi.org/10.20944/preprints202408.1815.v1
Sulistijono, I. A.; Yuniawan, A.; Barakbah, A. R.; Arief, Z.; Kubota, N. Holonomic Mobile Robot Navigation System Based on Collaboration Fuzzy Controller and Particle Swarm Optimization. Preprints2024, 2024081815. https://doi.org/10.20944/preprints202408.1815.v1
APA Style
Sulistijono, I. A., Yuniawan, A., Barakbah, A. R., Arief, Z., & Kubota, N. (2024). Holonomic Mobile Robot Navigation System Based on Collaboration Fuzzy Controller and Particle Swarm Optimization. Preprints. https://doi.org/10.20944/preprints202408.1815.v1
Chicago/Turabian Style
Sulistijono, I. A., Zainal Arief and Naoyuki Kubota. 2024 "Holonomic Mobile Robot Navigation System Based on Collaboration Fuzzy Controller and Particle Swarm Optimization" Preprints. https://doi.org/10.20944/preprints202408.1815.v1
Abstract
Currently, many health workers have died of being infected by dangerous infectious diseases. One solution is to prevent the spread of transmissions by minimizing contact between patients and staff or others using a mobile robot for delivering logistics services. We propose an approach by applies a navigation system using a position driver and obstacle avoidance using a fuzzy controller and particle swarm optimization for path planning in an unknown environment for a holonomic mobile robot. A Fuzzy Controller was used for obstacle avoidance so that the mobile robot could avoid obstacles using existing proximity sensors as the perception of the robot. In the path planning section, Particle Swarm Optimization is used to find intermediate points that must be reached by the mobile robot to reach their target. First, we performed simulation under the condition that the simulation resolution was similar to that of real conditions. It was found that the Trigonometric Function Adaptive PSO (TFAPSO) was better for finding the optimal solution based on the safest, shortest path, and lowest time. Finally, the holonomic mobile robot is tested directly in an unknown environment with the trajectory path generated by TFAPSO in real condition.
Keywords
holonomic mobile robot; fuzzy controller; position driver; obstacle avoidance; particle swarm optimization; path planning
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
