Swarm Robots Control System Based Fuzzy-PSO

In this paper describes swarm robots control design using combination Fuzzy logic and Particle swarm optimization algorithm. They can communicate with each other to achieve the target. Fuzzy Logic technique is used for navigating swarm robots in unknown environment and Particle Swarm Optimization (PSO) is used for searching and finding the best position of target. In this experiment utilize three identical robots with different color. Every robot has three infrared sensors, two gas sensors, 1 compass sensor and one X-Bee. A camera in the roof of robot arena is utilized to determine the position of each robot with color detection methods. Swarm robots and camera are connected to a computer which serves as an information center. From the experimental results the Fuzzy-PSO algorithm is able to control swarm robots, achieves the best target position in short time and produce smooth trajector

Swarm Robots Control System based Fuzzy-PSO

In this paper describes swarm robots control design using combination Fuzzy logic and Particle swarm optimization algorithm. They can communicate with each other to achieve the target. Fuzzy Logic technique is used for navigating swarm robots in unknown environment and Particle Swarm Optimization (PSO) is used for searching and finding the best position of target. In this experiment utilize three identical robots with different color. Every robot has three infrared sensors, two gas sensors, 1 compass sensor and one X-Bee. A camera in the roof of robot arena is utilized to determine the position of each robot with color detection methods. Swarm robots and camera are connected to a computer which serves as an information center. From the experimental results the Fuzzy-PSO algorithm is able to control swarm robots, achieves the best target position in short time and produce smooth trajector

Comparative Analysis Multi-Robot Formation Control Modeling Using Fuzzy Logic Type 2 – Particle Swarm Optimization

Multi-robot is a robotic system consisting of several robots that are interconnected and can communicate and collaborate with each other to complete a goal. With physical similarities, they have two controlled wheels and one free wheel that moves at the same speed. In this Problem, there is a main problem remaining in controlling the movement of the multi robot formation in searching the target. It occurs because the robots have to create dynamic geometric shapes towards the target. In its movement, it requires a control system in order to move the position as desired. For multi-robot movement formations, they have their own predetermined trajectories which are relatively constant in varying speeds and accelerations even in sudden stops. Based on these weaknesses, the robots must be able to avoid obstacles and reach the target. This research used Fuzzy Logic type 2 – Particle Swarm Optimization algorithm which was compared with Fuzzy Logic type 2 – Modified Particle Swarm Optimization and Fuzzy Logic type 2 – Dynamic Particle Swarm Optimization. Based on the experiments that had been carried out in each environment, it was found that Fuzzy Logic type 2 - Modified Particle Swarm Optimization had better iteration, time and resource and also smoother robot movement than Fuzzy Logic type 2 – Particle Swarm Optimization and Fuzzy Logic Type 2 - Dynamic Particle Swarm Optimization