scholarly journals Gerak Robot Berkaki Dua menggunakan ROS dan RViz sebagai Visualisasi Interaktif

2021 ◽  
Vol 9 (1) ◽  
pp. 43
Author(s):  
ADI SUCIPTO ◽  
RADEN SANGGAR DEWANTO ◽  
DADET PRAMADIHANTO
Keyword(s):  
Operating System ◽  
Success Rate ◽  
Operating Systems ◽  
Three Dimensional ◽  
Biped Robot ◽  
Sensor Data ◽  
Average Error ◽  
Robot Operating System ◽  
Robot Technology ◽  
The Right

ABSTRAKPengembangan sistem operasi pada bidang robotika telah menjadi fokus utama pada era ini. Salah satu perkembangan sistem operasi pada teknologi robot saat ini adalah Robot Operating System (ROS) dengan RViz. ROS merupakan sistem operasi berbasis library dan beberapa tools untuk mengembangkan suatu program pada robot, sedangkan RViz merupakan visualisasi tiga dimensi yang dapat digunakan untuk memvisualisasikan robot dan data sensor dynamixel. Pada Penelitian kali ini, peneliti membuat simulasi beberapa gerakan yang dilakukan pada RViz dan kemudian diimplementasikan pada robot. Tingkat keberhasilan dari perencanaan gerakan ini memiliki rata rata error sebesar 1.8%. Gerakan condong ke kiri memiliki rata-rata error sebesar 0.83%. Gerakan condong ke kanan memiliki rata-rata error sebesar 0.84%. Gerakan mengangkat satu kaki memiliki rata-rata error sebesar 1.71%. Gerakan kaki kanan ke depan memiliki rata-rata error sebesar 3.83%.Kata kunci: Robot Berkaki Dua, Robot Operating System (ROS), RViz (rosvisualization), Dynamixel Controller, Data Sensor Dynamixel. ABSTRACTThe development of operating systems in the field of robotics has become the main focus of this era. One of the operating system developments in robot technology today is the Robot Operating System (ROS) with RViz. ROS is a library-based operating system and several tools for developing a program on robots, while RVIZ is a three-dimensional visualization that can be used to visualize robots and dynamixel sensor data. In this study, researchers made a simulation of some of the movements carried out on RViz and then implemented on robots. The success rate of planning this movement has an average error of 1.8%. Leaning to the left has an average error of 0.83%. Leaning to the right has an average error of 0.84%. One leg lift has an average error of 1.71%. The movement of the right foot forward has an average error of 3.83%.Keywords: Biped Robot, Robot Operating System (ROS), RViz (Ros-Visualization), Dynamixel Controller, Sensor Dynamixel Data.

scholarly journals Analysis of the use of sensors in mobile devices with modified operating systems

2017 ◽  
Vol 5 ◽  
pp. 193-199
Author(s):  
Mateusz Dobrowolski ◽  
Michał Dobrowolski ◽  
Piotr Kopniak
Keyword(s):  
Operating System ◽  
Mobile Devices ◽  
Operating Systems ◽  
Sensor Data ◽  
Light Sensor ◽  
Orientation Sensor

This publication concentrate on the posibility of the use of sensors in mobile devices with modified operating systems. Presented research focuses on Android devices. The gyroscope, the accelerometer, the orientation sensor and the light sensor data was acquired with use of Physics Toolbox Sensor software. The research has been conducted on two mobile devices of Xiaomi under control of six different kinds of operating system. Measured values were compared to values recorded by very accurate, reference sensors

An architecture for multi-robot localization and mapping in the Gazebo/Robot Operating System simulation environment

SIMULATION ◽  
2017 ◽  
Vol 93 (9) ◽  
pp. 771-780 ◽  
Author(s):  
Erkan Uslu ◽  
Furkan Çakmak ◽  
Nihal Altuntaş ◽  
Salih Marangoz ◽  
Mehmet Fatih Amasyalı ◽  
...  
Keyword(s):  
Operating System ◽  
Three Dimensional ◽  
Simulation Environment ◽  
Urban Search And Rescue ◽  
Mapping Algorithms ◽  
Robot Operating System ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Dimensional Simulation ◽  
Multi Robot

Robots are an important part of urban search and rescue tasks. World wide attention has been given to developing capable physical platforms that would be beneficial for rescue teams. It is evident that use of multi-robots increases the effectiveness of these systems. The Robot Operating System (ROS) is becoming a standard platform for the robotics research community for both physical robots and simulation environments. Gazebo, with connectivity to the ROS, is a three-dimensional simulation environment that is also becoming a standard. Several simultaneous localization and mapping algorithms are implemented in the ROS; however, there is no multi-robot mapping implementation. In this work, two multi-robot mapping algorithm implementations are presented, namely multi-robot gMapping and multi-robot Hector Mapping. The multi-robot implementations are tested in the Gazebo simulation environment. Also, in order to achieve a more realistic simulation, every incremental robot movement is modeled with rotational and translational noise.

The 3D Numerical Simulation of Flow Field on Y-Shape Flaring Gate Piers Combined with Stepped Spillway

2013 ◽  
Vol 864-867 ◽  
pp. 2200-2206
Author(s):  
Ju Rui Yang ◽  
Xiao Xia Hou ◽  
Qiu Yue Zhang
Keyword(s):  
Water Vapor ◽  
Flow Velocity ◽  
Three Dimensional ◽  
Iterative Solution ◽  
Average Error ◽  
Stepped Spillway ◽  
Two Phase ◽  
Vof Model ◽  
The Right ◽  
Volume Method

The energy dissipater of stepped spillway combined with flaring gate pier is widely used in china's hydraulic engineering. The finite volume method is applied to discrete analysis, with the RNG turbulence model and VOF model of water vapor two-phase, iterative solution of geometry reconstruction format unsteady flow to generate free surface. Adopting structured grid for geometric shape, numerically simulated the water vapor two-phase flow from the reservoir to stilling basin. The parabolic water-vapor interface , overall flow pattern, water wings, section depth and other hydraulic characteristics was produced by simulating the three-dimensional flow field.Compared the simulated results of water depth, flow velocity in stilling pool, the board pressure with experiment data, the average error is: the left side depth of 3 # table hole of 7.1%, and the right of 7.4%; the underside flow velocity of 3 # table hole of 5%;1 # table hole stilling pool board pressure of 7.6%,3 # table hole stilling pool board pressure of 6.6%.

Tool to Perform Software-in-the-Loop through Robot Operating System

2015 ◽  
Vol 713-715 ◽  
pp. 2391-2394
Author(s):  
Mauricio Mauledoux ◽  
Crhistian C.G. Segura ◽  
Oscar F. Aviles
Keyword(s):  
Operating System ◽  
Operating Systems ◽  
Control Strategy ◽  
Experimental Validation ◽  
Magnetic Levitation ◽  
Raspberry Pi ◽  
Control Loop ◽  
Robot Operating System ◽  
State Variable ◽  
Loop Feedback

This article describes the use of Software-in-the-loop (SIL) and Robot Operating System (ROS) as tools for controller implementation and simulation of discrete-time plants is exposed. For experimental validation a magnetic levitation plant is used, this is modeled using Lagrange obtaining a nonlinear model which is linearized. Thus this model is discretized using a Tustin transformation for subsequent implementation of the control loop. Feedback state variable is implemented as control strategy for experimental validation on a system (Raspberry-Pi / fit-PC, Matlab / PC). We chose to use ROS as it is available for computers running operating systems based on Linux, as used in various embedded systems commercially available com the Fit-PC, Beagle-Board and Raspberry-Pi, ROS occupies low disk space (basic installation), programming is done in C ++ allowing more thorough use of the hardware. For testing three modules (node) implemented; "Reference_node" which is responsible for requesting the user to the desired position and transmit it to the next node, "control_node" is responsible for carrying out checks, which receives as inputs the reference (desired position) and the output of the plant (position current), and which outputs the control signal (u), finally "plant_node" is the node that simulates the behavior of the plant.

scholarly journals Reactive Navigation on Natural Environments by Continuous Classification of Ground Traversability

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6423
Author(s):  
Jorge L. Martínez ◽  
Jesús Morales ◽  
Manuel Sánchez ◽  
Mariano Morán ◽  
Antonio J. Reina ◽  
...  
Keyword(s):  
Operating System ◽  
Mobile Robot ◽  
Autonomous Vehicles ◽  
Sensor Data ◽  
Natural Environments ◽  
Reactive Navigation ◽  
Robot Operating System ◽  
Low Speeds

Reactivity is a key component for autonomous vehicles navigating on natural terrains in order to safely avoid unknown obstacles. To this end, it is necessary to continuously assess traversability by processing on-board sensor data. This paper describes the case study of mobile robot Andabata that classifies traversable points from 3D laser scans acquired in motion of its vicinity to build 2D local traversability maps. Realistic robotic simulations with Gazebo were employed to appropriately adjust reactive behaviors. As a result, successful navigation tests with Andabata using the robot operating system (ROS) were performed on natural environments at low speeds.

scholarly journals Autonomous navigation and mapping in a simulated environment

2021 ◽  
Author(s):  
Benjamin Christie ◽  
Osama Ennasr ◽  
Garry Glaspell
Keyword(s):  
Operating System ◽  
Autonomous Navigation ◽  
Three Dimensional ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Robot Operating System ◽  
Simulated Environment ◽  
Localization And Mapping ◽  
Environment Exploration ◽  
Rgbd Cameras

Unknown Environment Exploration (UEE) with an Unmanned Ground Vehicle (UGV) is extremely challenging. This report investigates a frontier exploration approach, in simulation, that leverages Simultaneous Localization And Mapping (SLAM) to efficiently explore unknown areas by finding navigable routes. The solution utilizes a diverse sensor payload that includes wheel encoders, three-dimensional (3-D) LIDAR, and Red, Green, Blue and Depth (RGBD) cameras. The main goal of this effort is to leverage frontier-based exploration with a UGV to produce a 3-D map (up to 10 cm resolution). The solution provided leverages the Robot Operating System (ROS).

Yale-CMU-Berkeley dataset for robotic manipulation research

2017 ◽  
Vol 36 (3) ◽  
pp. 261-268 ◽  
Author(s):  
Berk Calli ◽  
Arjun Singh ◽  
James Bruce ◽  
Aaron Walsman ◽  
Kurt Konolige ◽  
...  
Keyword(s):  
Operating System ◽  
High Resolution ◽  
Three Dimensional ◽  
Real Life ◽  
Point Clouds ◽  
Robotic Manipulation ◽  
Test Results ◽  
Robot Operating System ◽  
System Node ◽  
Rgb Images

In this paper, we present an image and model dataset of the real-life objects from the Yale-CMU-Berkeley Object Set, which is specifically designed for benchmarking in manipulation research. For each object, the dataset presents 600 high-resolution RGB images, 600 RGB-D images and five sets of textured three-dimensional geometric models. Segmentation masks and calibration information for each image are also provided. These data are acquired using the BigBIRD Object Scanning Rig and Google Scanners. Together with the dataset, Python scripts and a Robot Operating System node are provided to download the data, generate point clouds and create Unified Robot Description Files. The dataset is also supported by our website, www.ycbbenchmarks.org , which serves as a portal for publishing and discussing test results along with proposing task protocols and benchmarks.

scholarly journals Simulation of gymnastics performance based on MEMS sensor

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Bingxin Chen ◽  
Lifei Kuang ◽  
Wei He
Keyword(s):  
Measurement Error ◽  
Dimensional Space ◽  
Tracking System ◽  
Three Dimensional ◽  
Sensor Data ◽  
Average Error ◽  
Dynamic Link Library ◽  
Virtual Character ◽  
Main Research ◽  
Mems Sensor

AbstractThe development and progress of multi-sensor data fusion theory and methods have also laid the foundation for the research of human body posture tracking system based on inertial sensing. The main research in this paper is the simulation of gymnastics performance based on MEMS sensors. In the preprocessing to reduce noise interference, this paper mainly uses median filtering to remove signal glitches. This article uses virtual character models for gymnastics performances. The computer receives sensor data from the sink node of the motion capture device through a Bluetooth communication module. The unit calculates the quaternion output from the dynamic link library of sensor data processing, calculates the rotation amount and coordinate offset of each sensor node’s limb, and uses the character model to realize the real-time rendering of the virtual character model. At the same time, it controls the storage of sensor data, the drive of the model, and the display of the graphical interface. When a gesture action is about to occur, a trigger signal is given to the system to mark the beginning of the action, so as to obtain the initial data of each axis signal of the MEMS sensor. When the gesture action is completed, give the system a signal to end the action. Mark the end of the action, so that you can capture the original signal data during the beginning and end of the gesture action. In order to ensure the normal communication between PS and PL, it is necessary to test the key interfaces involved. Because the data received by the SPI acquisition module is irregular, it is impossible to verify whether the data is wrong, so the SPI acquisition module is replaced with a module that automatically increments data, and the IP core is generated, and a test platform is built for testing. The data shows that the average measurement error of X-axis displacement of the space tracking system is 8.17%, the average measurement error of Y-axis displacement is 7.51%, the average measurement error of Z-axis displacement is 9.72%, and the average error of three-dimensional space measurement is 8.7%. The results show that the MEMS sensor can accurately recognize the action with high accuracy.

F1_Tenth_Course_Report_Group_C

2018 ◽  
Author(s):  
Yi Chen ◽  
Sagar Manglani ◽  
Roberto Merco ◽  
Drew Bolduc
Keyword(s):  
Operating System ◽  
Autonomous Driving ◽  
Software Tools ◽  
Simulation Environment ◽  
Robot Operating System ◽  
Available Information

In this paper, we discuss several of major robot/vehicle platforms available and demonstrate the implementation of autonomous techniques on one such platform, the F1/10. Robot Operating System was chosen for its existing collection of software tools, libraries, and simulation environment. We build on the available information for the F1/10 vehicle and illustrate key tools that will help achieve properly functioning hardware. We provide methods to build algorithms and give examples of deploying these algorithms to complete autonomous driving tasks and build 2D maps using SLAM. Finally, we discuss the results of our findings and how they can be improved.

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