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This paper discusses the odor localization using Fuzzy logic algorithm. The concentrations of the source that is sensed by the gas sensors are used as the inputs of the fuzzy. The output of the Fuzzy logic is used to determine the PWM (Pulse Width Modulation) of driver motors of the robot. The path that the robot should track depends on the PWM of the right and left motors of the robot. When the concentration...
This paper discusses a design of fuzzy logic algorithm in a robot. This algorithm is useful for the robot in seeking and reaching the target. The robot is also accomplished with an ability to avoid obstacles. Although the fuzzy rule that is embedded to the robot is very simple, it gives a good result in target seeking and obstacles avoiding task. The originality of this research is an approach to...
This paper presents is to design swarm formation control using behavior-based for real robots. It's consists of two behavior are avoiding obstacles and formation keeping. During formation, the robots are also desired to coordinate among themselves to avoid collisions and keep formation the desired distance between each other. This design is using Fuzzy Logic Controller System. In this work a fuzzy...
The aim of this work is to design a mobile robot using WPT (Wi-Fi Position Techniques) method. The design in this research is intended to determine the mobile robot position by using the Wi-Fi module to determine the coordinates of position as determination to reach the target position and to find the strength of the detected Wi-Fi signal. Mobile robot is using to get the results of the strength of...
This paper represents a cooperative searching strategy of swarm robot in finding and localizing odor source. 3 robots, namely: Cyborg G11, Cyborg G12, and Cyborg G13 were used as agents that form the swarm in this research. The cooperation among the robots trough communication has shown the success of the swarm in odor searching. When one of the agents in swarm robot detected a high concentration...
This paper discusses about the process of classifying odor using Support Vector Machine. The training data was taken using a robot that ran in indoor room. The odor was sensed by 3 gas sensors, namely: TGS 2600, TGS 2602, and TGS 2620. The experimental environment was controlled and conditioned. The temperature was kept between 27.5 0C to 30.5 0C and humidity was in the range of 65%–75 %. After simulation...
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