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SISTEM KOORDINASI DAN KECERDASAN BUATAN UNTUK STRATEGI BERTANDING PADA ROBOT SOCCER Widagdo, Prabancoro Adhi Catur; Rachmawati, Ermita Dwi; Chulafaurrosyda, Renita; Sulistijono, Indra Adji
Program Kreativitas Mahasiswa - Penelitian PKM-P 2013
Publisher : Ditlitabmas, Ditjen DIKTI, Kemdikbud RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (532.71 KB)

This research presents an autonomous control system of multi-agent robot soccer. The control system is used to realize the holonomic movement of 2 agent robot to make their trajectory. A movement of multi-agent robot soccer is based on visualization by a camera which located above the robot field. A visualization involves process of color detection, such as marker detection and ball detection. The results will processed into coordinate data to make conversion become angle data, distance data and heading angle of agent robot. The generated data will broadcast serially and the agent robot will receive the appropriate data to make a movement. The movement data will processed by the Distribution of Vector Equation. The resulting motion are linear motion, angular motion, and mixed of linear and angular. The results of the system that have been made are work properly, which have a record of agent robot movement, the Â error dataÂ is 1,44%. Â Keywords: Broadcast, Color Detection, Distribution of Vector, Holonomic, Multi-agent Robot Soccer,Â Â Â Â Â Â Â Â Â Â Â Trajectory.

Fast Response Three Phase Induction Motor Using Indirect Field Oriented Control (IFOC) Based On Fuzzy-Backstepping Fauzi, Rizana; Happyanto, Dedid Cahya; Sulistijono, Indra Adji
EMITTER International Journal of Engineering Technology Vol 3, No 1 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (540.017 KB)

Induction Motor in Electrical drive system at a accelleration speed for example in electric cars have a hard speed setting is set on a wide range, causing an inconvenience for motorists and a fast response is required any change of speed. It is necessary for good system performance in control motor speed and torque at low speed or fast speed response, which is operated by Indirect Field Oriented Control (IFOC). Speed control on IFOC methods should be better to improving the performance of rapid response in the induction motor. In this paper presented a method of incorporation of Fuzzy Logic Controller and Backstepping (Fuzzy-Backstepping) to improve the dynamically response speed and torque in Induction Motor on electric car, so we get smoothness at any speed change and braking as well as maximum torque of induction motor. Test results showed that Fuzzy-Backstepping can increase the response to changes speed in electric car. System testing is done with variations of the reference point setting speed control system, the simulation results of the research showed that the IFOC method is not perfect in terms of induction motor speed regulation if itâs not use speed control. Fuzzy-Backstepping control is needed which can improve the response of output, so that the induction motor has a good performance, small oscillations when start working up to speed reference.Keywords: Fuzzy-Backstepping, IFOC, induction motor

Fast Response Three Phase Induction Motor Using Indirect Field Oriented Control (IFOC) Based On Fuzzy-Backstepping Fauzi, Rizana; Happyanto, Dedid Cahya; Sulistijono, Indra Adji
EMITTER International Journal of Engineering Technology Vol 3, No 1 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Induction Motor in Electrical drive system at a accelleration speed for example in electric cars have a hard speed setting is set on a wide range, causing an inconvenience for motorists and a fast response is required any change of speed. It is necessary for good system performance in control motor speed and torque at low speed or fast speed response, which is operated by Indirect Field Oriented Control (IFOC). Speed control on IFOC methods should be better to improving the performance of rapid response in the induction motor. In this paper presented a method of incorporation of Fuzzy Logic Controller and Backstepping (Fuzzy-Backstepping) to improve the dynamically response speed and torque in Induction Motor on electric car, so we get smoothness at any speed change and braking as well as maximum torque of induction motor. Test results showed that Fuzzy-Backstepping can increase the response to changes speed in electric car. System testing is done with variations of the reference point setting speed control system, the simulation results of the research showed that the IFOC method is not perfect in terms of induction motor speed regulation if itâs not use speed control. Fuzzy-Backstepping control is needed which can improve the response of output, so that the induction motor has a good performance, small oscillations when start working up to speed reference.Keywords: Fuzzy-Backstepping, IFOC, induction motor

Application of Artificial Neural Networks in Modeling Direction Wheelchairs Using Neurosky Mindset Mobile (EEG) Device Siswoyo, Agus; Arief, Zainal; Sulistijono, Indra Adji
EMITTER International Journal of Engineering Technology Vol 5, No 1 (2017)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

The implementation of Artificial Neural Network in prediction the direction of electric wheelchair from brain signal input for physical mobility impairment.. The control of the wheelchair as an effort in improving disabled person life quality. The interaction from disabled person is helping in relation to social life with others. Because of the mobility impairment, the wheelchair with brain signal input is made. This wheel chair is purposed to help the disabled person and elderly for their daily activity. ANN helps to develop the mapping from input to target. ANN is developed in 3 level: input level, one hidden level, and output level (6-2-1). There are 6 signal from Neurosky Mindset sensor output, Alpha1, Alpha2, Raw signal, Total time signal, Attention Signal, and Meditation signal. The purpose of this research is to find out the output value from ANN: value in turning right, turning left, and forward. From those outputs, we can prove the relevance to the target. One of the main problem that interfering with success is the problem of proper neural network training. Arduino uno is chosen to implement the learning program algorithm because it is a popular microcontroller that is economic and efficient. The training of artificial neural network in this research uses 21 data package from raw data, Alpha1, Aplha2, Meditation data, Attention data, total time data. At the time of the test there is a value of Mean square Error(MSE) at the end of training amounted to 0.92495 at epoch 9958, value a correlation coefficient of 0.92804 shows that accuracy the results of the training process good.Â Â Keywords: Navigation, Neural network, Real-time training, ArduinoÂ

Automatic Samples Selection Using Histogram of Oriented Gradients (HOG) Feature Distance Salfikar, Inzar; Sulistijono, Indra Adji; Basuki, Achmad
EMITTER International Journal of Engineering Technology Vol 5, No 2 (2017)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v5i2.182

Finding victims at a disaster site is the primary goal of Search-and-Rescue (SAR) operations. Many technologies created from research for searching disaster victims through aerial imaging. but, most of them are difficult to detect victims at tsunami disaster sites with victims and backgrounds which are look similar. This research collects post-tsunami aerial imaging data from the internet to builds dataset and model for detecting tsunami disaster victims. Datasets are built based on distance differences from features every sample using Histogram-of-Oriented-Gradient (HOG) method. We use the longest distance to collect samples from photo to generate victim and non-victim samples. We claim steps to collect samples by measuring HOG feature distance from all samples. the longest distance between samples will take as a candidate to build the dataset, then classify victim (positives) and non-victim (negatives) samples manually. The dataset of tsunami disaster victims was re-analyzed using cross-validation Leave-One-Out (LOO) with Support-Vector-Machine (SVM) method. The experimental results show the performance of two test photos with 61.70% precision, 77.60% accuracy, 74.36% recall and f-measure 67.44% to distinguish victim (positives) and non-victim (negatives).

Perancangan Sistem Navigasi Otonom pada Behavior Based Hexapod Robot Wicaksono, Handy; Prihastono, Prihastono; Anam, Khairul; Effendi, Rusdhianto; Sulistijono, Indra Adji; Kuswadi, Son; Jazidie, Achmad; Sampei, Mitsuji
Jurnal Teknik Elektro Vol 8, No 2 (2008): SEPTEMBER 2008
Publisher : Institute of Research and Community Outreach

Six legged robot (hexapod) has advantage over wheeled robot in its capability to walk over rough terrain. In this paper, hexapod mobility will be tested in order to measure its performance in walk through beam and stair. Behavior based architecture will be used in hexpod, so it can react quickly. Autonomous navigation application has been chosen here in order to prove that the architecture is running well. From simulation result, it can be seen that behavior based hexapod robot has good mobility (it can walk through obstacle that has 10 cm height) and it can accomplish its task to avoid the obstacles and find the light source. Abstract in Bahasa Indonesia: Robot berkaki enam (hexapod) memiliki kelebihan dibanding robot beroda dalam hal kemampuannya melewati daerah tidak rata. Pada penelitian ini, mobilitas pergerakan hexapod akan diuji untuk mengetahui performanya dalam melewati balok dan tangga. Supaya dapat bereaksi dengan cepat, maka arsitektur behavior based akan digunakan pada hexapod. Aplikasi navigasi otonom dipilih untuk menunjukkan bahwa arsitektur tersebut berjalan dengan baik. Dari hasil simulasi nampak bahwa behavior based hexapod robot memiliki mobilitas yang baik (mampu melewati halangan setinggi maksimal 10 cm) dan dapat menyelesaikan tugasnya untuk menghindari halangan dan menemukan sumber cahaya. Kata kunci: mobilitas, hexapod robot, behavior based architecture, sistem navigasi otonom

Optimasi Sistem Navigasi Robot Bencana dengan Algoritma Bug dan Jaringan Syaraf Tiruan Son Kuswadi; Ardelia Natasya GO; Muhamad Nasyir Tamara; Indra Adji Sulistijono
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5, No 5: Oktober 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Agar robot bencana bisa melaksanakan tugas tertentu pada medan yang tak beraturan dan tidak diketahui keadaannya secara dinamis, harus memiliki kemampuan pemetaan. Berdasarkan peta yang telah dibuat, maka robot bisa bergerak sesuai dengan peta tersebut. Makalah ini membahas implementasi pemetaan dan navigasi robot, dengan menggunakan algoritma buguntuk membuat lintasan yang dapat menghindari halangan. Lintasan tersebut kemudian dioptimalkan dengan menggunakan jaringan syaraf tiruan untuk memilih lintasan terpendek. Metode yang diusulkan ini kemudian diuji baik menggunakan perangkat lunak dan eksperimen di medan laboratorium. Abstract In order to perform certain task in a cluttered and unknown dynamic field, a disaster robot should have mapping capability. Based on the map, then robot will move accordingly. This paper describes the implementation of mapping and navigation of the robot by using bug algorithm to avoid the obstacle. The paths optimized by using artificial neural networks to select the shortest one. The proposed method then implemented both by simulation and experimental in lab scale field.

Fast Response Three Phase Induction Motor Using Indirect Field Oriented Control (IFOC) Based On Fuzzy-Backstepping Rizana Fauzi; Dedid Cahya Happyanto; Indra Adji Sulistijono
EMITTER International Journal of Engineering Technology Vol 3 No 1 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Induction Motor in Electrical drive system at a accelleration speed for example in electric cars have a hard speed setting is set on a wide range, causing an inconvenience for motorists and a fast response is required any change of speed. It is necessary for good system performance in control motor speed and torque at low speed or fast speed response, which is operated by Indirect Field Oriented Control (IFOC). Speed control on IFOC methods should be better to improving the performance of rapid response in the induction motor. In this paper presented a method of incorporation of Fuzzy Logic Controller and Backstepping (Fuzzy-Backstepping) to improve the dynamically response speed and torque in Induction Motor on electric car, so we get smoothness at any speed change and braking as well as maximum torque of induction motor. Test results showed that Fuzzy-Backstepping can increase the response to changes speed in electric car. System testing is done with variations of the reference point setting speed control system, the simulation results of the research showed that the IFOC method is not perfect in terms of induction motor speed regulation if itâ€™s not use speed control. Fuzzy-Backstepping control is needed which can improve the response of output, so that the induction motor has a good performance, small oscillations when start working up to speed reference.Keywords: Fuzzy-Backstepping, IFOC, induction motor

Application of Artificial Neural Networks in Modeling Direction Wheelchairs Using Neurosky Mindset Mobile (EEG) Device Agus Siswoyo; Zainal Arief; Indra Adji Sulistijono
EMITTER International Journal of Engineering Technology Vol 5 No 1 (2017)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

The implementation of Artificial Neural Network in prediction the direction of electric wheelchair from brain signal input for physical mobility impairment.. The control of the wheelchair as an effort in improving disabled person life quality. The interaction from disabled person is helping in relation to social life with others. Because of the mobility impairment, the wheelchair with brain signal input is made. This wheel chair is purposed to help the disabled person and elderly for their daily activity. ANN helps to develop the mapping from input to target. ANN is developed in 3 level: input level, one hidden level, and output level (6-2-1). There are 6 signal from Neurosky Mindset sensor output, Alpha1, Alpha2, Raw signal, Total time signal, Attention Signal, and Meditation signal. The purpose of this research is to find out the output value from ANN: value in turning right, turning left, and forward. From those outputs, we can prove the relevance to the target. One of the main problem that interfering with success is the problem of proper neural network training. Arduino uno is chosen to implement the learning program algorithm because it is a popular microcontroller that is economic and efficient. The training of artificial neural network in this research uses 21 data package from raw data, Alpha1, Aplha2, Meditation data, Attention data, total time data. At the time of the test there is a value of Mean square Error(MSE) at the end of training amounted to 0.92495 at epoch 9958, value a correlation coefficient of 0.92804 shows that accuracy the results of the training process good.Â Â Keywords: Navigation, Neural network, Real-time training, ArduinoÂ

Automatic Samples Selection Using Histogram of Oriented Gradients (HOG) Feature Distance Inzar Salfikar; Indra Adji Sulistijono; Achmad Basuki
EMITTER International Journal of Engineering Technology Vol 5 No 2 (2017)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v5i2.182

Finding victims at a disaster site is the primary goal of Search-and-Rescue (SAR) operations. Many technologies created from research for searching disaster victims through aerial imaging. but, most of them are difficult to detect victims at tsunami disaster sites with victims and backgrounds which are look similar. This research collects post-tsunami aerial imaging data from the internet to builds dataset and model for detecting tsunami disaster victims. Datasets are built based on distance differences from features every sample using Histogram-of-Oriented-Gradient (HOG) method. We use the longest distance to collect samples from photo to generate victim and non-victim samples. We claim steps to collect samples by measuring HOG feature distance from all samples. the longest distance between samples will take as a candidate to build the dataset, then classify victim (positives) and non-victim (negatives) samples manually. The dataset of tsunami disaster victims was re-analyzed using cross-validation Leave-One-Out (LOO) with Support-Vector-Machine (SVM) method. The experimental results show the performance of two test photos with 61.70% precision, 77.60% accuracy, 74.36% recall and f-measure 67.44% to distinguish victim (positives) and non-victim (negatives).

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