Next Article in Journal
Nonlinear Response of Tilting Pad Journal Bearings to Harmonic Excitation
Next Article in Special Issue
Vibration-Based Experimental Identification of the Elastic Moduli Using Plate Specimens of the Olive Tree
Previous Article in Journal
Wind Turbine Yaw Control Optimization and Its Impact on Performance
Previous Article in Special Issue
Tie-System Calibration for the Experimental Setup of Large Deployable Reflectors
Open AccessArticle

Unmanned Ground Vehicle Modelling in Gazebo/ROS-Based Environments

1
MEID4 Srl, via Giovanni Paolo II, 84084 Fisciano (SA), Italy
2
Department of Industrial Engineering, University of Salerno, via Giovanni Paolo II, 84084 Fisciano (SA), Italy
*
Author to whom correspondence should be addressed.
Machines 2019, 7(2), 42; https://doi.org/10.3390/machines7020042
Received: 31 March 2019 / Revised: 5 June 2019 / Accepted: 9 June 2019 / Published: 14 June 2019
The fusion of different technologies is the base of the fourth industrial revolution. Companies are encouraged to integrate new tools in their production processes in order to improve working conditions and increase productivity and production quality. The integration between information, communication technologies and industrial automation can create highly flexible production models for products and services that can be customized through real-time interactions between consumer, production and machinery throughout the production process. The future of production, therefore, depends on increasingly intelligent machinery through the use of digital systems. The key elements for future integrated devices are intelligent systems and machines, based on human–machine interaction and information sharing. To do so, the implementation of shared languages that allow different systems to dialogue in a simple way is necessary. In this perspective, the use of advanced prototyping tools like Open-Source programming systems, the development of more detailed multibody models through the use of CAD software and the use of self-learning techniques will allow for developing a new class of machines capable of revolutionizing our companies. The purpose of this paper is to present a waypoint navigation activity of a custom Wheeled Mobile Robot (WMR) in an available simulated 3D indoor environment by using the Gazebo simulator. Gazebo was developed in 2002 at the University of Southern California. The idea was to create a high-fidelity simulator that gave the possibility to simulate robots in outdoor environments under various conditions. In particular, we wanted to test the high-performance physics Open Dynamics Engine (ODE) and the sensors feature present in Gazebo for prototype development activities. This choice was made for the possibility of emulating not only the system under analysis, but also the world in which the robot will operate. Furthermore, the integration tools available with Solidworks and Matlab-Simulink, well known commercial platforms of modelling and robotics control respectively, are also explored. View Full-Text
Keywords: wheeled mobile robot; robotics; multibody dynamics; gazebo; Matlab wheeled mobile robot; robotics; multibody dynamics; gazebo; Matlab
Show Figures

Figure 1

MDPI and ACS Style

Rivera, Z.B.; De Simone, M.C.; Guida, D. Unmanned Ground Vehicle Modelling in Gazebo/ROS-Based Environments. Machines 2019, 7, 42.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop