This hexapod fire extinguisher robot is constructed based on technological developments evolving very rapidly, especially in the field of robotics technology. The hexapod robot has become a big concern in the development of robotics technology with many existing contests of hexapod robot. This hexapod fire extinguisher robot is designed based on Fuzzy Mamdani Logic. The navigation of fire extinguisher hexapod robot is based on distance detection with ultrasonic sensor determining the movement of robot’s legs utilizing Fuzzy Logic. A fire extinguisher hexapod robot will explore a tunnel arena having several rooms created for the robot to explore. This hexapod robot uses Arduino as a microcontroller and uses 18 servo motors in which each foot requires 3 servo motors. Moreover, good navigation will be aimed by utilizing Fuzzy Logic in the hexapod robot design. The application of many rules on Fuzzy Logic makes the better navigation; furthermore, the results present the ultrasonic sensor having an average error of 1.256%, the Fuzzy Logic applied showing 0% error, and the overall success rate presenting approximately 80%.
The purpose of this study is to create an early fire detection system based on fuzzy logic. This system provides early warning of fire hazards, reduces the risk of casualties, and is able to be implemented to a wider scale or scope. This system consists of a multisensor to detect fire, smoke and temperature in the room. KY-026 sensor as fire detection, MQ-9 sensor as smoke detection and DS18b20 sensor as temperature detection. The results of the sensor readings are processed by a microcontroller implanted with a Sugeno fuzzy logic method. Sensor input is processed through several stages, namely fuzzification, rule evaluation and deffuzification. The output from this system is a condition value between 1 and 5. The average error of testing between Arduino modules with Matlab is 0.99%.
AbstrakSistem pemantau dan kendali memiliki fungsi untuk merekap data dan mengakses perangkat ruangan yang terpasang pada setiap titik pemasangan. Komunikasi yang digunakan adalah komunikasi nirkabel ZigBee dengan menggunakan topologi mesh. Coordinator merupakan pusat data yang terhubung langsung ke komputer, dan hasilnya ditampilkan dalam bentuk HMI, sehingga seorang operator dapat dengan mudah memantau dan mengendalikan perangkat-perangkat yang terpasang pada ruangan. Perangkat ruangan terdiri dari sensor diantaranya PIR, arus, pendeteksi asap dan sidik jari, yang kesemuannya terhubung dengan arduino yang bertugas sebagai pemroses data untuk membentuk protokol-protokol yang akan dikirim dari titik end device ke coordinator. Hasil pengujian jarak pengiriman antara titik pemasangan ZigBee maksimal 93 meter tanpa halangan dan 30 meter dengan halangan. Adapun throughput pengiriman data dari end device dan simulator ke titik coordinator akan semakin besar jika delay diperkecil dan paket data diperbesar dalam setiap pengirimannya. Hasil dari packet loss pada pengujian didapatkan 4,94 %, ini dikarenakan faktor floating yang terjadi pada arduino. Kata kunci: ZigBee, topologi mesh, throughput, packet loss Abstract The monitoring and control system has the function of recording data and accessing the installed room equipment. The communication used is ZigBee wireless communication with mesh topology. The coordinator is a data center connected directly to the computer, and the results are displayed in the form
This research aimed for planning and implementation mobile robot with control in an automatic and manual that used sensor ultrasonic as a detection object and also used Bluetooth technology as a control in a manual mobile robot via smartphone. This mobile robot can detect obstacle around it and have a navigation system. If ultrasonic sensor detect an obstacle, then mobile robot will turn and running in a automatic without hit or object around it. This mobile robot can do communication bi-directional, sensoring process, and recognize ID or character from controller. This mobile robot application consists of circuit controller and mobile robot circuit controller consists of smartphone and Bluetooth application. Mobile robot consists of arduino mega 2560 , driver module, sensor ultrasonic, and Bluetooth HC-05 module. AbstrakPenelitian ini bertujuan untuk melakukakan perencanaan dan implementasi mobile robot dengan kontrol secara otomatis dan manual yaitu menggunakan sensor ultrasonic sebagai pendeteksi objek dan juga menggunakan teknologi Bluetooth sebagai pengendalian manual mobile robot via smartphone . Mobile robot ini mampu mendeteksi penghalang yang ada disekitarnya dan memiliki sistem navigasi. Jika sensor ultrasonik mendeteksi adanya penghalang, maka robot akan berbelok dan berjalan secara otomatis tanpa menabrak penghalang ataupun benda yang berada disekitarnya. Mobile robot ini mampu melakukan komunikasi bidirectional , proses sensoring , dan mengenali ID ataupun karakter dari controller. Aplikasi mobile robot ini terdiri dari rangkaian controller dan mobile robot. Rangkaian controller terdiri dari smartphone dan aplikasi Bluetooth. Rangkaian mobile robot terdiri dari Arduino mega 2560 ,modul driver, sensor ultrasonic, dan modul Bluetooth HC-05.
<p class="Abstrak"> </p><p class="Abstrak">Sistem keamanan yang bertujuan sebagai sistem monitoring pada <em>smart home</em> seperti memonitoring pengguna laboratorium, perpustakaan, atau ruangan penyimpanan dan peminjaman peralatan praktek di program studi suatu kampus, ruang penyimpanan senjata, hingga rumah tinggal, memerlukan sekuritas yang handal untuk memudahkan identifikasi pengguna ruangan atau pencegahan dari tindak pencurian, maka dirancang sistem monitoring melalui pengenalan citra sidik jari menggunakan sensor ZFM60, jaringan syaraf tiruan dan MySQL. Tujuannya agar di dapat pola yang relevan dari citra dan mengeliminasi informasi atau variabel yang tidak relevan. Metode yang digunakan yaitu <em>experimental</em>, terdiri dari pengumpulan data sidik jari, perancangan sistem pengolahan citra, pembuatan dan pengujian <em>hardware</em> dan <em>software</em>, serta implementasi sistem. Hasil proses pengenalan atau klarifikasi citra sidik jari melalui GUI Matlab, nilai <em>error</em> hasil pengolahan dan pelatihan citra sidik jari dengan jaringan syaraf tiruan, digunakan sebagai ciri citra dan disimpan sebagai <em>data base</em> pada MySQL, kemudian dibandingkan dengan nilai <em>error</em> citra sidik jari baru yang di klarifikasi. Nilai citra yang dapat dikenali berada diantara -0,0005 hingga 0,0005, diluar batas tersebut merupakan citra yang tidak dikenali. Selisih (nilai <em>error</em>) antara ciri citra yang tersimpan pada <em>data base</em> dan ciri citra yang diklarifikasi menghasilkan nilai <em>error </em>yang kecil yaitu < 0.0005, menunjukkan jaringan syaraf tiruan <em>backpropagation</em> handal diimplementasikan pada pengenalan sidik jari untuk melatih pola citra dari sidik jari. Konfigurasi jaringan yaitu maksimal <em>epoch</em> = 3000, <em>learning rate</em> = 1, target <em>error</em> = 0.1, <em>hidden layer</em> = 17. Pelatihan jaringan syaraf tiruan pada konfigurasi tersebut menghasilkan nilai <em>error</em> terkecil dari ciri citra sebesar 0.0000085.</p><p class="Abstrak"> </p><p class="Judul2"><strong><em>Abstract</em></strong><em> </em></p><p class="Judul2"><em><br /></em></p><p class="Judul2"><em>The security system that aims as a monitoring system in smart home such as monitoring laboratory users, libraries, or storage rooms and borrowing practical equipment in the study program of a campus, weapons storage room, to a residence, requires reliable securities to facilitate identification of room users or prevention from theft, it is designed a monitoring system through fingerprint image recognition using ZFM60 sensors, artificial neural networks and MySQL. The goal is to get relevant patterns from the image and eliminate irrelevant information or variables. The method used is experimental, consisting of fingerprint data collection, image processing system design, hardware and software manufacturing and testing, and system implementation. The result of the process of recognition or clarification of fingerprint images through the Matlab GUI, the error value of processing and training of fingerprint images with artificial neural networks, is used as a feature of the image and stored as a data base on MySQL, then compared with the error value of the new fingerprint image that is clarified. The recognizable image value is between -0,0005 to 0,0005, beyond this limit is an unrecognized image. The difference (error value) between the characteristics of the image stored in the data base and the clarified image feature produces a small error value of <0.0005, indicating a reliable backpropagation artificial neural network is implemented in fingerprint recognition to train the image pattern of fingerprints. Network configuration is maximum epoch = 3000, learning rate = 1, target error = 0.1, hidden layer = 17. Artificial neural network training in the configuration produces the smallest error value of the image characteristics of 0.0000085.</em></p>
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