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<p><span lang="EN-US"><span style="font-size: small;">In this paper, coordination among individual of swarm robot in communicating to maintain the safe distance between robots was analyzed.  Each robot coordinates their movements to avoid obstacles and moving simultaneously. Evaluation of swarm robot performance is analyzed in this paper, namely: the coordination among robots to share information in safe distance determination.  In controlling the coordination of motion, each robot has a sensor that provides several inputs about its surrounding environment. Fuzzy logic control in this paper allows uncertain input, and produces unlimited commands to control motion direction with speed settings according to environmental conditions. In this experiment, it is obtained that the size of the environment affects the coordination of robots.</span></span><em></em></p>

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Navigation is one of the typical problem domains occurred in studying swarm robot. This task needs a special ability in avoiding obstacles.  This research presents the navigation techniques using type 1 fuzzy logic and interval type 2 fuzzy logic. A comparison of those two fuzzy logic performances in controlling swarm robot as tools for complex problem modeling, especially for path navigation is presented in this paper.  Each hierarchical of fuzzy logic shows its advantages and disadvantages.  For testing the robustness of type-1 fuzzy logic and interval type-2 fuzzy logic algorithms, 3 robots for the real swarm robot experiment are used.  Each is equipped with one compass sensor, three distance sensors, and one X-Bee communication module.  The experimental results show that type-2 fuzzy logic has better performance than type-1 fuzzy logic.