Establishing ROS on Humanoid Soccer Robot-BarelangFC Software System

Humanoid robot is built on several sub-programs or systems which is integrated to each main programs in order to command the robot to move as a soccer player. Each main programs namely as a movement system, a visual sense system (vision), a sub-controller system, and a game strategy. Currently, each of main system constructed using different programming language, for instance: the vision system used python while the others used C and LUA for the movement kinematics. Employing different programming language will affect to response system because each of main system need to be integrated using socket in the beginning process. Robot response will be slow and cost a lot of memory usage. Therefore, in this paper will present a migrating process into robot operating system (ROS) and switch all the robot main system into python language. The integrated program will be examined in real-time application while the robot moved on the field. We used a python ROS in order to make the robot play autonomously on the field.Robot humanoid dikembangkan dari beberapa sub program atau sistem yang terintegrasi pada setiap program utama untuk memerintahkan robot bergerak selayaknya seperti pemain sepak bola. Masing-masing program utama terdiri atas sistem gerak, sistem indra visual (vision system), sistem sub-kontroler, dan strategi permainan. Saat ini, masing-masing sistem utama didesain menggunakan bahasa pemrograman yang berbeda, misalnya: sistem visi menggunakan python sedangkan yang lain menggunakan C dan LUA untuk kinematika gerakan. Penggunaan bahasa pemrograman yang berbeda akan mempengaruhi respon sistem karena masing-masing sistem utama perlu diintegrasikan menggunakan socket pada proses awal. Respon robot akan lambat dan menghabiskan banyak penggunaan memori. Oleh karena itu, dalam makalah ini akan disajikan proses migrasi ke dalam sistem operasi robot (ROS) dan mengalihkan semua sistem utama robot ke dalam bahasa python. Program terintegrasi akan diperiksa secara real-time aplikasi saat robot bergerak di lapangan. Serta menggunakan python ROS untuk membuat robot dapat bermain secara mandiri di lapangan

The ROS: Kinetic Kame for Humanoid Robot BarelangFC

A collaborative robot such as humanoid robot which able to play soccer consist tons of software framework such as servo controller, vision system, strategy receiver and transmitter, sensors, and coordination system. All these frameworks needed to be integrated to simplify the command of creating the complexity of the robot behaviors. To overcome these problems, the Robot Operating System (ROS) can be implemented on each robot. This paper presented the implementation of the ROS: Kinetic Kame in order to integrated the whole framework which is existed in the robot. To verify the performance of this system, some experiments has been done in real-time application. From the experimental results, the ROS: Kinetic Kame able to integrate each software framework of the robot in very good response.Sebuah robot kolaboratif seperti robot humanoid yang mampu bermain sepak bola terdiri dari banyak sekali framework software seperti servo controller, vision system, receiver and transmitter strategi, sensor, dan sistem koordinasi. Semua framework software ini perlu diintegrasikan untuk menyederhanakan perintah dalam menciptakan kompleksitas robot behaviour. Untuk mengatasi permasalahan tersebut, maka Robot Operating System (ROS) dapat diimplementasikan pada setiap robot. Makalah ini memaparkan implementasi ROS: Kinetic Kame untuk mengintegrasikan seluruh framework yang ada pada robot. Untuk memverifikasi kinerja sistem ini, beberapa percobaan telah dilakukan dalam aplikasi real-time. Dari hasil percobaan, ROS: Kinetic Kame mampu mengintegrasikan setiap framework software robot dengan respon yang sangat bai

Industry 4.0: Hand Recognition on Assembly Supervision Process

In the assembly industry, the process of assembling components is very important in order to produce a quality product. Assembly of components should be carried out sequentially based on the standards set by the company. For companies that still operate the assembly process manually by employee, sometimes errors occur in the assembly process, which can affect the quality of production. In order to be carried out the assembly process according to the procedure, a system is needed that can detect employee hands when carrying out the assembly process automatically. This study proposes an artificial intelligence-based real-time employee hand detection system. This system will be the basis for the development of an automatic industrial product assembly process to welcome the Industry 4.0. To verify system performance, several experiments were carried out, such as; detecting the right and left hands of employees and detecting hands when using accessories or not. From the experimental results it can be concluded that the system is able to detect the right and left hands of employees well with the resulting FPS average of 15.4.Pada industri perakitan, proses merakit komponen merupakan hal yang sangat penting guna menghasilkan produk yang berkualitas. Perakitan komponen hendaklah dilakukan secara urut berdasarkan standar yang telah ditentukan oleh perusahaan. Bagi perusahaan yang masih menggunakan proses perakitan secara manual yakni dengan menggunakan tenaga manusia, terkadang terjadi kesalahan dalam proses perakitan, sehingga dapat mempengaruhi kualitas produksi. Agar proses perakitan dapat dilakukan sesuai prosedur, maka diperlukan sebuah sistem yang dapat mendeteksi tangan karyawan ketika melakukan proses perakitan secara otomatis. Penelitian ini mengusulkan sistem pendeteksian tangan karyawan secara real-time berbasis kecerdasan buatan. Sistem ini akan menjadi dasar untuk pengembangan proses perakitan produk industri secara otomatis untuk menyambut industri 4.0. Untuk memverifikasi kinerja sistem, beberapa percobaan dilakukan yaitu mendeteksi tangan kanan dan kiri karyawan serta mendeteksi tangan ketika menggunakan aksesoris atau tidak. Dari hasil percobaan dapat disimpulkan bahwa sistem mampu mendeteksi tangan kanan dan kiri karyawan dengan baik dengan rata-rata FPS yang dihasilkan adalah 15.4

Real-time Coordinate Estimation for Self-Localization of the Humanoid Robot Soccer BarelangFC

In implementation, of the humanoid robot soccer consists of more than three robots when played soccer on the field. All the robots needed to be played the soccer as human done such as seeking, chasing, dribbling and kicking the ball. To do all of these commands, it is required a real-time localization system so that each robot will understand not only the robot position itself but also the other robots and even the object on the field’s environment. However, in real-time implementation and due to the limited ability of the robot computation, it is necessary to determine a method which has fast computation and able to save much memory. Therefore, in this paper we presented a real-time localization implementation method using the odometry and Monte Carlo Localization (MCL) method. In order to verify the performance of this method, some experiment has been carried out in real-time application. From the experimental result, the proposed method able to estimate the coordinate of each robot position in X and Y position on the field.Dalam implementasinya, robot humanoid soccer terdiri lebih dari tiga robot di lapangan ketika sedang bermain bola. Semua robot diharapkan dapat memainkan sepak bola seperti manusia seperti mencari, mengejar, menggiring bola dan menendang bola. Untuk melakukan semua perintah tersebut, diperlukan sistem lokalisasi real-time sehingga setiap robot tidak hanya memahami posisi robotnya sendiri tetapi juga robot-robot lain bahkan objek yang berada di sekitar lapangan. Namun dalam implementasi real-time dan karena keterbatasan kemampuan komputasi robot, diperlukan suatu metode komputasi yang cepat dan mampu menghemat banyak memori. Oleh karena itu, dalam makalah ini menyajikan metode implementasi lokalisasi real-time dengan menggunakan metode odometry and Monte Carlo Localization (MCL). Untuk memverifikasi kinerja metode ini, beberapa percobaan telah dilakukan dalam aplikasi real-time. Dari hasil percobaan, metode yang diusulkan mampu mengestimasi koordinat posisi robot pada posisi X dan Y di lapangan ketika sedang bermain bola

Tiny-YOLO distance measurement and object detection coordination system for the BarelangFC robot

A humanoid robot called BarelangFC was designed to take part in the Kontes Robot Indonesia (KRI) competition, in the robot coordination division. In this division, each robot is expected to recognize its opponents and to pass the ball towards a team member to establish coordination between the robots. In order to achieve this team coordination, a fast and accurate system is needed to detect and estimate the other robot’s position in real time. Moreover, each robot has to estimate its team members’ locations based on its camera reading, so that the ball can be passed without error. This research proposes a Tiny-YOLO deep learning method to detect the location of a team member robot and presents a real-time coordination system using a ZED camera. To establish the coordinate system, the distance between the robots was estimated using a trigonometric equation to ensure that the robot was able to pass the ball towards another robot. To verify our method, real-time experiments was carried out using an NVDIA Jetson NX Xavier, and the results showed that the robot could estimate the distance correctly before passing the ball toward another robot

Penempatan Pendeteksi Masker Untuk Pencegahan Penyebaran Covid di Kampus dan Pelabuhan

At this time the world has been hit by the Covid-19 pandemic since 2019. The government advises the Indonesian people to follow the Health protocol. One of them is to wear a mask when we travel to public places. Some public places that are difficult to avoid include schools/campuses and ports. The people of the Riau Archipelago are very dependent on sea transportation modes. The movement of people is very massive in both places. Therefore, people are expected to always be disciplined in using masks in crowded places. To ensure and remind the public to always wear a mask is rather difficult. So we developed a mask detection device and stored it in public places. This tool leverages artificial intelligence technology with deep learning. This tool works very well, it can remind people who don't wear masks, even those who wear masks that aren't right.Beberapa tempat umum sulit untuk dihindari terkait pembatasan aktifitas akbiat Covid-19, diantaranya adalah sekolah/kampus dan pelabuhan. Perpindahan manusia sangatlah massif di kedua tempat tersebut. Untuk memastikan dan mengingatkan masyarakat untuk selalu mengunakan masker agak sulit, kami mengembangkan alat pendeteksi masker dan menyimpannya di tempat-tempat umum. Pengabdian ini menerapkan hasil penelitian dan disimpan di dua tempat yaitu di kampus dan di pelabuhan. Alat ini memanfaatkan teknologi kecerdasan buatan dengan deep learning. Sistem ini terdiri dari kamera, untuk mendeteksi orang tanpa masker, sebuah komputer untuk mengolah data, sebuah layar untuk menampilkan tangkapan kamera beserta speaker. Dimana jika terdeteksi orang yang tidak mengunakan masker ataupun yang tidak memakai secara baik, maka akan terlihat dilayar dan akan diingatkan secara audio. Alat ini berfungsi sangat baik, dapat mengingatkan orang-orang yang tidak mengunakan masker bahkan yang mengunakan masker yang tidak benar. Hal ini sesuai dengan hasil wawancara dengan pengelola kedua tempat tadi

Object Detection and Pose Estimation with RGB-D Camera for Supporting Robotic Bin-Picking

ABSTRAKTujuan dari penelitian ini adalah untuk mendeteksi objek dan mengestimasi pose objek menggunakan kamera RGB-D. Dalam penelitian ini, kami mengusulkan pemrosesan data pada citra RGB dan citra depth saja, tanpa menggunakan point cloud, seperti pada umumnya. Metode yang diusulkan mendeteksi posisi dan orientasi objek menggunakan DRBox-v2 dari Region of Interest (ROI), yang sebelumnya diperoleh dari pendeteksian pada penanda ArUco. Hasil deteksi objek kemudian diskalakan dan digunakan pada citra depth untuk mendapatkan perkiraan posisi dan orientasi objek. Dari sisi pendeteksi objek, usulan metode memperoleh nilai Average Precision (AP) sebesar 0,740. Sedangkan untuk estimator pose, usulan metode menghasilkan kesalahan posisi rata-rata 13,36 mm dan kesalahan orientasi rata-rata 0,75 derajat. Metode yang diusulkan berpotensi menjadi alternatif sistem deteksi objek dan estimasi pose pada kamera RGB-D yang tidak memerlukan pemrosesan point cloud dan tidak memerlukan model referensi objek.Kata kunci: deteksi objek, estimasi pose, DRBox, ArUco, bin-picking ABSTRACTThis study aims to detect objects and estimate the object's pose using an RGB-D camera. In this study, we proposed data processing on RGB images and depth images only, without using point clouds, as in general. The proposed method detected the object's position and orientation using the DRBox-v2 from the Region of Interest (ROI), which was previously obtained from detecting ArUco markers. The object detection results were then scaled and used in the depth image to get the object's approximate position and orientation. In object detection, the proposed method obtained an Average Precision (AP) value of 0.740. As for the pose estimator, our method generated an average position error of 13.36 mm and an average orientation error of 0.75 degrees. Therefore, this method can be an alternative object detection and pose estimation system on an RGB-D camera that does not require point cloud processing and an object reference model.Keywords: object detection, pose estimation, DRBox, ArUco, bin-pickin

Indoor Localization Using Positional Tracking Feature of Stereo Camera on Quadcopter

During the maneuvering of most unmanned aerial vehicles (UAVs), the GPS is one of the sensors used for navigation. However, this kind of sensor cannot handle indoor navigation applications well. Using a camera might be the answer to performing indoor navigation using its coordinate system. In this study, we considered indoor navigation applications using the ZED2 stereo camera for the quadcopter. To use the ZED 2 camera as a navigation sensor, we first transformed its coordinates into the North, East, down (NED) system to enable the drone to understand its position and maintain stability in a particular position. The experiment was performed using a real-time application to confirm the feasibility of this approach for indoor localization. In the real-time application, we commanded the quadcopter to follow triangular and rectangular paths. The results indicated that the quadcopter was able to follow the paths and maintain its stability in specific coordinate positions

Overview: Types of Lower Limb Exoskeletons

Researchers have given attention to lower limb exoskeletons in recent years. Lower limb exoskeletons have been designed, prototype tested through experiments, and even produced. In general, lower limb exoskeletons have two different objectives: (1) rehabilitation and (2) assisting human work activities. Referring to these objectives, researchers have iteratively improved lower limb exoskeleton designs, especially in the location of actuators. Some of these devices use actuators, particularly on hips, ankles or knees of the users. Additionally, other devices employ a combination of actuators on multiple joints. In order to provide information about which actuator location is more suitable; a review study on the design of actuator locations is presented in this paper. The location of actuators is an important factor because it is related to the analysis of the design and the control system. This factor affects the entire lower limb exoskeleton’s performance and functionality. In addition, the disadvantages of several types of lower limb exoskeletons in terms of actuator locations and the challenges of the lower limb exoskeleton in the future are also presented in this paper

Real-Time Identification of Knee Joint Walking Gait as Preliminary Signal for Developing Lower Limb Exoskeleton

An exoskeleton is a device used for walking rehabilitation. In order to develop a proper rehabilitation exoskeleton, a user’s walking intention needs to be captured as the initial step of work. Moreover, every human has a unique walking gait style. This work introduced a wearable sensor, which aimed to recognize the walking gait phase, as the fundamental step before applying it into the rehabilitation exoskeleton. The sensor used in this work was the IMU sensor, used to recognize the pitch angle generated from the knee joint while the user walks, as information about the walking gait cycle, before doing the investigation on how to identify the walking gait cycle. In order to identify the walking gait cycle, Neural Network has been proposed as a method. The gait cycle identification was generated to recognize the gait cycle on the knee joint. To verify the performance of the proposed method, experiments have been done in real-time application. The experiments were carried out with different processes such as walking on a flat floor, climbing up, and walking down stairs. Five subjects were trained and tested using the system. The experiments showed that the proposed method was able to recognize each gait cycle for all users as they wore the sensor on their knee joints. This study has the potential to be applied on an exoskeleton rehabilitation robot as a further research experiment