Articles
<![CDATA[Swarm Robots Control System based Fuzzy-PSO ]]>
<![CDATA[Siti Nurmaini]]>;
<![CDATA[Siti Zaiton M. Hashim]]>;
<![CDATA[Agus Triadi]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 1: EECSI 2014 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v1.349
<![CDATA[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 trajectory]]>
<![CDATA[Swarm Robots Communication-base Mobile Ad-Hoc Network (MANET) ]]>
<![CDATA[Bambang Tutuko]]>;
<![CDATA[Siti Nurmaini]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 1: EECSI 2014 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v1.361
<![CDATA[This paper describes the swarm robots communication and control base Mobile ad-hoc network (MANET). MANET is a source of codes which migrate the network, collects and exchanges information of network nodes. In this work, the communication networks, which do not rely on fixed, preinstalled communication devices like base stations or predefine communication cells. Communications standards are considered in this work use the ad-hoc network such as Wireless LAN, X-Bee/Zig-Bee and Internet platform. All standards are integrated on swarm robots for real experiments. For finding the target, Particle swarm optimization (PSO) algorithm is proposed to control the real swarm robots communication in unknown experiment. As a results swarm robots-base MANET use PSO algorithm produce past response to find the target and swarm robots can move in the group without collision.]]>
<![CDATA[Gas Leak Localization Using Mobile Sensor Networks ]]>
<![CDATA[Bambang Tutuko]]>;
<![CDATA[Siti Nurmaini]]>;
<![CDATA[Agus Triadi]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 2: EECSI 2015 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v2.782
<![CDATA[A swarm robotics localization strategybased on deliberates fuzzy and swarm behavior isproposed in this paper. Localization is the processof determining the positions of robots or targetsin whole swarms environment. To localize thetarget in real environment, experiment isconducted utilize three identical robots withdifferent color as mobile sensor node. Fuzzy logicand swarm behavior are keeping the swarmrobots position and direction with a certaindistance to the target position.]]>
<![CDATA[Pattern Recognition Approach for Formation Control for Swarm Robotics Using Fuzzy-Kohonen Networks ]]>
<![CDATA[Siti Nurmaini]]>;
<![CDATA[Bambang Tutuko]]>;
<![CDATA[Aditya Aditya]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 2: EECSI 2015 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v2.802
<![CDATA[Recent advances in robotics have started makingit feasible to deploy large numbers of inexpensive robotsfor tasks such as surveillance and search. However,coordination of multiple robots to accomplish such tasksremains a challenging problem. This paper describes newalgorithm Fuzzy-Kohonen Networks (FKN) to achieve realtimereactive formation control for swarm robots.]]>
<![CDATA[Brief Review on Formation Control of Swarm Robot ]]>
<![CDATA[Ade Silvia Handayani]]>;
<![CDATA[Siti Nurmaini]]>;
<![CDATA[Iryadi Yani]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 2: EECSI 2015 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v2.804
<![CDATA[This paper presented review formation control ofswarm robot. Recently the problems formation control of swarmrobots has attracted much attention, and several formationcontrol schemes were proposed based on various strategies. Theformation control strategies to solved these problem on swarmrobots, with considering regulation concept in control theory.Swarm intelligence algorithms takes the full of advantages of thefeature of swarm robotics, and provides a great solution forproblem formation control on swarm robots.]]>
<![CDATA[Review on Odor Localization ]]>
<![CDATA[Nyayu Latifah Husni]]>;
<![CDATA[Siti Nurmaini]]>;
<![CDATA[Irsyadi Yani]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 2: EECSI 2015 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v2.807
<![CDATA[In this paper, the importance of odor localization isexplained. The researchers that investigated the experimentsand applications of odor localization using static sensors, mobilesensors (that were integrated in single robot, multi robot, andswarm robot) are described. However, there are some difficultiesfaced by the researchers in applying the mobile robots in the realsituation, such as: the speed of mobile robots are not as fast asthe odor patches transporting and the use of more than onesensor in mobile robot can make noises or errors. In the future,the plume finding in the uncertain environment and thechallenges mentioned above will be the authors’ consideration.]]>
<![CDATA[Target Tracking in Mobile Robot under Uncertain Environment using Fuzzy Logic Controller ]]>
<![CDATA[Ade Silvia Handayani]]>;
<![CDATA[Tresna Dewi]]>;
<![CDATA[Nyayu Latifah Husni]]>;
<![CDATA[Siti Nurmaini]]>;
<![CDATA[Irsyadi Yani]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v4.981
<![CDATA[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 the rules that can simplify the task by creating faster track for the robot in uncertain environment. ]]>
<![CDATA[Odor Localization using Gas Sensor for Mobile Robot ]]>
<![CDATA[Nyayu Latifah Husni]]>;
<![CDATA[Ade Silvia Handayani]]>;
<![CDATA[Siti Nurmaini]]>;
<![CDATA[Irsyadi Yani]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v4.1087
<![CDATA[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 in the right side of the robot is higher than the middle and the left side, the fuzzy logic will give decision to the robot to move to the right. In that condition, the left motor is in the high speed condition and the right motor is in slow speed condition. Therefore, the robot will move to the right. The experiment was done in a conditioned room using a robot that is equipped with 3 gas sensors. Although the robot is still needed some improvements in accomplishing its task, the result shows that fuzzy algorithms are effective enough in performing odor localization task in mobile robot.]]>
<![CDATA[Differential-Drive Mobile Robot Control Design based-on Linear Feedback Control Law ]]>
<![CDATA[Siti Nurmaini]]>;
<![CDATA[Kemala Dewi]]>;
<![CDATA[Bambang Tutuko Tutuko]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 3: EECSI 2016 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v3.1115
<![CDATA[This paper deals with the problem of how to control differential driven mobile robot with simple control law. When mobile robot moves from one position to another to achieve a position destination, it always produce some errors. Therefore, a mobile robot requires a certain control law to drive the robot’s movement to the position destination with a smallest possible error. In this paper, in order to reduce position error, a linear feedback control is proposed with pole placement approach to regulate the polynoms desired. The presented work leads to an improved understanding of differential-drive mobile robot (DDMR)-based kinematics equation, which will assist to design of suitable controllers for DDMR movement . The result show by using the linier feedback control method with pole placement approach the position error is reduced and fast convergence is achieved.]]>
<![CDATA[Information Framework of Pervasive Real Time Monitoring System: Case of Peat Land Forest Fires and Air Quality in South Sumatera, Indonesia ]]>
<![CDATA[Siti Nurmaini]]>;
<![CDATA[Reza Firsandaya Malik]]>;
<![CDATA[Deris Stiawan]]>;
<![CDATA[Firdaus Firdaus]]>;
<![CDATA[Saparudin Saparudin]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 3: EECSI 2016 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v3.1163
<![CDATA[The information framework aims to holistically address the problems and issues posed by unwanted peat and land fires within the context of the natural environment and socio-economic systems. Informed decisions on planning and allocation of resources can only be made by understanding the landscape. Therefore, information on fire history and air quality impacts must be collected for future analysis. This paper proposes strategic framework based on technology approach with data fusion strategy to produce the data analysis about peat land fires and air quality management in in South Sumatera. The research framework should use the knowledge, experience and data from the previous fire seasons to review, improve and refine the strategies and monitor their effectiveness for the next fire season. Communicating effectively with communities and the public and private sectors in remote and rural landscapes is important, by using smartphones and mobile applications. Tools such as one-stop information based on web applications, to obtain information such as early warning to send and receive fire alerts, could be developed and promoted so that all stakeholders can share important information with each other.]]>
