Kontrol Attitude Unmanned Ground Vehicle (UGV) menggunakan Backpropagation Neural Network

Unmaned Ground Vehicle (UGV) merupakan teknologi kendaraan darat tanpa awak yang berguna untuk mempermudah pekerjaan manusia dalam berbagai bidang seperti transportasi, aktivitas logistik industri, search and resque, pertahanan dan keamanan, juga beberapa bidang lainnya. Pengendalian attitude menjadi permasalahan karena membutuhkan ketelitian akibat adanya pengaruh kecepatan. Selain itu, bagaimana UGV tersebut mengikuti jalur yang ditentukan juga memerlukan pengendalian attitude yang optimal. Penelitian ini bertujuan untuk merancang dan menguji performa serta untuk mengatahui tingkat keberhasilan dan keakuratan pengendalian UGV menggunakan algoritma Backpropagaion Neural Network. Dari hasil pengujian didapatkan bahwa algoritma ini berhasil mengikuti data uji yang diberikan dengan nilai MSE yang kecil

Metode Algoritma Genetika dengan Sistem Fuzzy Logic untuk Penentuan Parameter Pengendali PID

Proportional-Integral-Derivative ( PID) represents the popular controller which is frequently used in industry and instrumentation equipments. Although in simple design procedure, tuning of PID parameters ( Kp, KiAnd Kd) to get the optimal value is not easy and simple. Genetic Algorithm method with the Fuzzy Logic system is used to get the PID parameters with optimal performance result. Genetic Algorithm is a method that used to solve a problem of optimization based on selection and genetic evolution, while system of fuzzy logic is used to determine the geneticparameter like crossover probability and mutation. In this research, Genetic Algorithm with the Fuzzy Logic system is forming model of direct-current motor to get the parameters of PID controller based on value of mean square error ( MSE). This Examination yielded the stable response system with the value MSE gyrate 0.0027 until 0.0028 and steady state error gyrate 0.004 until 0.001

Comparison of Control Methods PD, PI, and PID on Two Wheeled Self Balancing Robot

A robot must employ a suitable control method to obtain a good stability. The Two-Wheeled Self Balancing Robot in this paper is designed using a MPU-6050 IMU sensor module and ATmega128 microcontroller as its controller board. This IMU sensor module is employed to measure any change in the robot’s tilt angle based on gyroscope and accelerometer readings contained in the module. The tilt angle readings are then utilized as the setpoint on the control methods, namely PD (Proportional Derivative), PI (Proportional Integral), or PID (Proportional Integral Derivative). Based on the conducted testing results, the PID controller is the best control strategy when compared to the PD and PI control. With parameters of Kp = 14, Ki = 0005 and Kd = 0.1, the robot is able to adjust the speed and direction of DC motor rotation to maintain upright positions on flat surfaces

Penalaan Parameter Pengendali PID untuk Pengendalian Kecepatan Motor Arus Searah Menggunakan Metode Algoritma Genetika dan Jaringan Syaraf Tiruan

Dalam pemodelan dan pemecahan suatu masalah, banyak yang mendapatkan kesulitan dalam menemukan sebuah metode untuk melakukan pendekatan terhadap suatu masalah yang lebih optimal dan efisien. Beberapa metode telah dikembangkan untuk dapat digunakan dalam pemecahan berbagai permasalahan. Sebagian besar metode tersebut menerapkan prinsip probabilitas yang dianggap dapat meminimalisasi kesalahan. Pada penelitian ini dipergunakan Jaringan Syaraf Tiruan untuk menentukan parameter peluang pindah silang (Pc) dan peluang mutasi (Pm) yang terdapat pada Algoritma Genetika untuk menentukan parameter pengendali Proportional Integral Derivative (PID). Penelitian ini mengambil objek motor arus searah. Dari penelitian ini didapatkan hasil terbaik pada populasi 100 dengan parameter PID yaitu Kp bernilai 1.0309, Ki bernilai 25.9346 dan Kd bernilai 0.0186, dimana nilai fitnes terbaik, yaitu 0.22443 pada generasi ke 64, dengan nilai fitnes rata-rata 11.6918. Respon sistem yang dihasilkan juga tidak memiliki overshot, tidak memiliki peak time,  settling time 0.345 detik, dan rise time 10-90% sebesar 0.10977 detik. Sehingga dapat dikatakan bahwa penggunaan Jaringan Syaraf Tiruan yang dikombinasikan dengan Algoritma Genetika dalam menentukan parameter pengendali PID cukup berhasil

Road and Vehicles Detection System Using HSV Color Space for Autonomous Vehicle

Nowadays, an autonomous vehicle is one of the fastest-growing technologies. In its movements, the autonomous vehicle requires a good navigation system to run on the specified lane. One sensor that is often used in navigation systems is the camera. However, this camera is constrained by the process and its reading, especially to detect roads that are suitable for the vehicle's position. Thus, this research was conducted to detect the road and distance of nearby objects using the HSV color space method. From the test results, this research succeeded in detecting roads with an accuracy of 78.012 %, and an accuracy of 80% for the safe/unsafe area detection. The results also showed that the method achieved an accuracy of 80% and 74.76%for object detection and object distance detection, respectively. The results of this research implied that the HSV method wasquite good with fairly high accuracy to detect roads and vehicles

The Detection System of Helipad for Unmanned Aerial Vehicle Landing Using YOLO Algorithm

The challenge with using the Unmanned Aerial Vehicle (UAV) is when the UAV makes a landing. This problem can be overcome by developing a landing vision through helipad detection. This helipad detection can make it easier for UAVs to land accurately and precisely by detecting the helipad using a camera. Furthermore, image processing technology is used on the image produced by the camera. You Only Look Once (YOLO) is an image processing algorithm developed to detect objects in real-time, and it is the result of the development of one of the Convolutional Neural Network (CNN) algorithm methods. Therefore, in this study the YOLO method was used to detect a helipad in real-time. The models used in the YOLO algorithm were Mean-Shift and Tiny YOLO VOC. The Tiny YOLO VOC model performed better than the Mean-Shift method in detecting helipads. The test results obtained a confidence value of 91.1%, and the system processing speed reached 35 frames per second (fps) in bright conditions and 37 fps in dark conditions at an altitude of up to 20 meters

Deep learning with focal loss approach for attacks classification

The rapid development of deep learning improves the detection and classification of attacks on intrusion detection systems. However, the unbalanced data issue increases the complexity of the architecture model. This study proposes a novel deep learning model to overcome the problem of classifying multi-class attacks. The deep learning model consists of two stages. The pre-tuning stage uses automatic feature extraction with a deep autoencoder. The second stage is fine-tuning using deep neural network classifiers with fully connected layers. To reduce imbalanced class data, the feature extraction was implemented using the deep autoencoder and improved focal loss function in the classifier. The model was evaluated using 3 loss functions, including cross-entropy, weighted cross-entropy, and focal losses. The results could correct the class imbalance in deep learning-based classifications. Attack classification was achieved using automatic extraction with the focal loss on the CSE-CIC-IDS2018 dataset is a high-quality classifier with 98.38% precision, 98.27% sensitivity, and 99.82% specificity

DESIGN BUILDING OF AUTOMATED FOOTWEAR AUTOMATIC VEHICLES BASED ON MICROCONTROLLER AT89S51

Kondisi kepadatan lalu lintas kendaraan diperburuk dengan kondisi jalan yang kurang memadai akibat intensitas penggunaan jalan yang berlebihan. Biasanya untuk memperoleh data jumlah kendaraan yang lewat di jalan raya masih dilakukan dengan cara manual yaitu dengan menugaskan beberapa orang untuk berada di lapangan (tempat survey) dan menghitung setiap kendaraan yang lewat, kemudian dibagi dengan rentang waktu tertentu. Rancangan penghitung kendaraan otomatis menggunakan laser dan sensor fotodioda sebagai masukan data yang diproses menggunakan Mikrokontroler AT89S51 dan akan dikirim ke dalam basis data melalui komunikasi serial ke PC (personal computer). Aplikasi ini tidak memerlukan perhitungan manual karena sudah tertampil di PC dengan menggunakan program yang dibuat menggunakan software Visual Basic. Rancangan mampu mendeteksi pancaran sinar laser sampai jarak 7 meter dan ASCII “1” yang dikirim Mikrokontroler ke PC mampu dibaca, disimpan dan ditampilkan program DzulAT89S51Vb. Hasil pengujian lapangan didapat jumlah kendaraan yang terdeteksi adalah sebanyak 66 kendaraan roda empat yang waktu pengujian dari jam 16:33 – 18:13 dengan pembagian waktu per 10 menit. Dari pengujian tersebut didapat total keakuratan alat sebesar 95,45% dengan rata – rata keakuratan  sebesar 97,083%.The condition of vehicle very crowded traffic, that condition caused by high intensity and overload amount vehicle. Usually to get amount of vehicle in street still use manual counting,  use some people to count every vehicle that through the ways, every counting process divide by certain time.  the Automatics of vehicle counting use laser and photodiode sensors as data input that can be possessed by Microcontrollers AT89S51 and data was sent to basis data through serial communication to PC. This application can’t apply manual counting because PC has shown by display, helped by Visual Basic.  Design can detect laser until range 7 meters and ASCII “1” that transmitted microcontroller to PC can be read, saved and shown by DzulAT89S51Vb. The result of test, the amount of vehicle can be detected, 66 four-wheel vehicles with range from 16:33 until 18:33, time divides by 10 minutes. The accurate of devices reach 95,45%, with mean 97,083%

The Impact of Telemetry Received Signal Strength of IMU/GNSS Data Transmission on Autonomous Vehicle Navigation

This paper presents the effect of received signal strength on IMU/GNSS sensor data transmission for autonomous vehicle navigation. A pixhawk 2.1 flight controller is used to build the navigation system. Straight lines with back-and-forth routes were tested using two types of SiK telemetry: Holybro and RFD. The results of the tests show that when the RSSI value falls close to the receiver's sensitivity value, the readings of the gyro sensor data, accelerometer, magnetometer, and GNSS compass data are disturbed. When the RSSI signal collides with noise, the radio telemetry link is lost, affecting the accuracy of speed data and the orientation of autonomous vehicles. According to Cisco's conversion table, the highest RSSI on Holybro telemetry is -48 dBm, and the lowest is -103 dBm, with a receiver sensitivity of -117 and data reading at a distance of about 427 meters. While the highest RSSI value on RFD telemetry is -17 dBm and the lowest is -113 dBm, even the lowest value is above the receiver's sensitivity limit of -121 dBm with data readings at a distance of approximately 749.4 meters. RFD outperforms Holybro in terms of RSSI and sensitivity at low data rates. When reading distance data to reference distance data using Google Earth and ArcGIS, RFD telemetry has a higher accuracy, with an average accuracy of 98.8%