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Robots in indoor and outdoor environments
Published in Anil Sawhney, Mike Riley, Javier Irizarry, Construction 4.0, 2020
Bharadwaj R. K. Mantha, Borja Garcia de Soto, Carol C. Menassa, Vineet R. Kamat
The robotic platform, equipped with the iCreate base (widely known as TurtleBot) was chosen as the mobile data collection platform and sensors such as Cozir® CM 0199 (for temperature, humidity, and CO2 levels), HOBO U12 (for light and occupancy levels), Lutron (for natural light levels), and NinjaBlocks (for airspeed) were used for the data collection. Figure 16.7 shows the mobile robot with some of the following components, 1) TurtleBot – for navigating the indoor environment; 2) Onboard computing – to communicate with the TurtleBot; 3) Camera – for the TurtleBot to detect fiducial markers, localize, and estimate its relative pose in the indoor environment; 4) Remote laptop – to execute the corresponding autonomous navigation programs wirelessly; and 5) Sensors – for monitoring and data collection of various ambient parameters.
Software, simulation & control
Published in Arkapravo Bhaumik, From AI to Robotics, 2018
ROS is released under the terms of the BSD license, and is open source software. It is free for commercial and research use. ROS is meant for UBUNTU installation and in its early days had two of its own robots, the PR2 and Turtlebot. ROS was an extension of the STAIR project and it started off by porting in most of their functionalities and simulators from the player project. Currently, it has URDF models of nearly all important mobile robots. It can be partially ported into Windows and Mac OS-X it also supports open source hardware platforms such as Raspberry Pi, ODROID etc. and various sleek hardware as Microsoft Kinect, ASUS Xtion, Sphero and LEAP device. It supports a number of robots: PR2, Turtlebot (I and II), Corobot, Roomba, Care-O-Bot, LEGO Mindstorms, Shadow Robot, Billibot, RAVEN-2 surgical robot, REEM-C humanoid and various others. ROS has had 8 distribution releases over the last seven years and it is now maintained and developed by Open Source Robotic Foundation (OSRF).
Review of Autonomous Campus and Tour Guiding Robots with Navigation Techniques
Published in Australian Journal of Mechanical Engineering, 2022
Debajyoti Bose, Karthi Mohan, Meera CS, Monika Yadav, Devender K. Saini
TurtleBot 2 is a low-cost, ROS-enabled, Autonomous Robot available for developers and testing purposes. It can be used for educational purposes, office use, and restaurant purpose based on its configuration (Koubâa et al. 2016). There is an upgraded version available for TurtleBot 2 in all aspects is TurtleBot 3, which is manufactured in 3 varieties Waffle, Waffle pi, and Burger.
From Trajectory Tracking Control to Leader–Follower Formation Control
Published in Cybernetics and Systems, 2020
Khadir Lakhdar Besseghieur, Radosław Trębiński, Wojciech Kaczmarek, Jarosław Panasiuk
TURTLEBOT is an indoor mobile robot that is known as a differential drive robot. It consists of two drive wheels mounted on a common axis that can be driven either forward or backward. Its motion can be described by the following model in a plane Oxy:
From Sensor-Space to Eigenspace – A Novel Real-Time Obstacle Avoidance Method for Mobile Robots
Published in IETE Journal of Research, 2022
Shyba Zaheer, Tauseef Gulrez, Imthias Ahamed Thythodath Paramabath
We simulated FCE for a differential drive mobile robot in ROS and Gazebo on a PC with virtual machine (VM-ware) based on Ubuntu Linux Core 4 Quad 3.4 GHz CPU, and 16.0 GB memory. Localization algorithm was assumed to be ideal. The turtlebot robot has a LiDAR to detect obstacles, where as LiDAR's angular range is . The cylinderical obstacles are tracked using FCE algorithm. The environment of the simulation is composed of environmental obstacles as shown in Figure 9. The environment includes fixed and moving obstacles. The controller for the robot is a ROS that gets the sensor data as input, over ROS topics, using these inputs various actions are determined, which consequently outputs a ROS message to the differential drive plug-in of the robot. For experimental validation purposes, the robot model selected was kabuki Turtlebot-II, which is ROS based robot and can perform advanced robotic tasks such as mapping, localization, etc. with the following specifications: Maximum translational velocity: 70 cm/sMaximum rotational velocity: 180 deg/sPayload: 5 kg (hard floor), 4 kg (carpet)PC Connection: USB or via RX/TX pins of the parallel portMotor Overload Detection: disables power on detecting high current Odometry: 52 ticks/encoder rev, 2578.33 ticks/wheel rev, 11.7 ticks/mmSensor Data Rate: 50 Hz