Relevant bibliographies by topics / Gazebo and rviz

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Academic literature on the topic 'Gazebo and rviz'

Author: Grafiati
Published: 5 June 2025
Last updated: 24 June 2025

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Journal articles on the topic "Gazebo and rviz"

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Veluvolu, Harsha, Ganesh Y, Chandan Parthasarathy, Pranav Akkipeddi, and Sharanbassappa Patil. "Simulation of Autonomous Tractor using ROS." ARAI Journal of Mobility Technology 5, no. 1 (2025): 1464–73. https://doi.org/10.37285/ajmt.5.1.7.

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Farmers encounter a multitude of obstacles in agriculture, such as operating in challenging weather conditions and unpredictable surroundings, which can have a significant impact on sustainability and productivity. To tackle these hurdles, autonomous agricultural vehicles are indispensable. This paper introduces the digital modelling and testing of a self-driving tractor system tailored for agricultural tasks. By utilizing the Robot Operating System (ROS) and Gazebo simulator, the study delves into the incorporation of cutting-edge navigation algorithms, including the GMapping algorithm for Simultaneous Localization and Mapping (SLAM), Dijkstra algorithm, and the Dynamic Window Approach (DWA) for efficient path planning. The tractor prototype, developed using Fusion 360, undergoes thorough assessments to evaluate its mapping accuracy, localization precision, path planning effectiveness, and obstacle avoidance capabilities. The outcomes showcase successful mapping and localization, precise trajectory planning, adept obstacle avoidance, and visualization through RViz and Gazebo co-simulation. The study aims to propel the realm of autonomous agriculture by offering insights into the design, enhancement, and virtual implementation of autonomous tractor systems, ultimately enhancing the efficiency and sustainability of agricultural practices. Keywords: Autonomous Vehicles, Autonomous Tractors, Path Planning, GMapping, AMCL, ROS, Gazebo, RViz, Dijkstra’s Algorithm, DWA Path Planning, PID Controller, Simulation
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Pramod Thale, Sumegh, Mihir Mangesh Prabhu, Pranjali Vinod Thakur, and Pratik Kadam. "ROS based SLAM implementation for Autonomous navigation using Turtlebot." ITM Web of Conferences 32 (2020): 01011. http://dx.doi.org/10.1051/itmconf/20203201011.

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This paper presents the autonomous navigation of a robot using SLAM algorithm.The proposed work uses Robot Operating system as a framework.The robot is simulated in gazebo and Rviz used for data visualization.Gmapping package is used for mapping by utilizing laser and odometry data from various sensors.The Turtlebot provides open source software to perform navigation.
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Ali, Hassan, Dr. Manaf H.Kadhum, and Dr. Ali Fawzi AbdulKareem. "Visualizing Kinematics: Investigating the Impact of Altered Joint Angles on End-Effector Position and Orientation using Rviz and WidowX250-6DOF." Wasit Journal of Engineering Sciences 12, no. 2 (2024): 1–14. http://dx.doi.org/10.31185/ejuow.vol12.iss2.477.

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In recent years, the fields of robotics have witnessed major developments, and simulation tools have played an important role in facilitating research and development. The WidowX250-6DOF robotic arm and the Rviz visualization tool are used in this study to look at how altering joint angles affects the position and orientation of the end-effector. The main goal of this study is to incorporate ROS on a Linux platform in order to give the widowX-250 a simulation environment. Important tasks like the application of forward, position/trajectory control and inverse kinematics should be enabled by this platform. ROS, together with Gazebo, RViz, and MoveIt, are crucial technologies for this project. The goal of this research is to gain a better understanding of how variations in joint angles impact the positioning and orientation of the robot's end-effector. The research helps to improve robotic arm control tactics and applications in many different industries, including manufacturing, automation, and robotics.
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Ghazal, Mohammed Talal, Murtadha Al-Ghadhanfari, and Najwan Zuhair Waisi. "Simulation of autonomous navigation of turtlebot robot system based on robot operating system." Bulletin of Electrical Engineering and Informatics 13, no. 2 (2024): 1238–44. http://dx.doi.org/10.11591/eei.v13i2.6419.

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Complex system science has recently shifted its focus to include modeling, simulation, and behavior control. An effective simulation software built on robot operating system (ROS) is used in robotics development to facilitate the smooth transition between the simulation environment and the hardware testing of control behavior. In this paper, we demonstrate how the simultaneous localization and mapping (SLAM) algorithm can be used to allow a robot to navigate autonomously. The Gazebo is used to simulate the robot, and Rviz is used to visualize the simulated data. The G-mapping package is used to create maps using collected data from a variety of sensors, including laser and odometry. To test and implement autonomous navigation, a Turtlebot was used in a Gazebo-generated simulated environment. In our opinion, additional study on ROS using these important tools might lead to a greater adoption of robotics tests performed, further evaluation automation, and efficient robotic systems.
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Wang, Zhicheng, Qingzhang Wang, Lingling Shen, et al. "Multi-point Navigation Method for Intelligent Inspection Robots." Journal of Computing and Electronic Information Management 14, no. 2 (2024): 16–19. https://doi.org/10.54097/1zjue56e.

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A multi-point navigation method is introduced for intelligent inspection robots, aimed at enhancing efficiency and safety across various industries. The newly proposed WW algorithm improves the robots' fault tolerance and positioning accuracy. The study utilized tools such as the ROS operating system, Rviz, and Gazebo, as well as the Agilex Bunker MINI intelligent trolley and SCOUT MINI sensor platform. It discussed SLAM map construction with Cartographer and Gmapping algorithms and the AMCL positioning system. Additionally, the DWA algorithm for dynamic obstacle avoidance was introduced. The system design includes a Qt interface and Rviz interface for multi-point navigation and obstacle avoidance. The effectiveness of the WW algorithm was verified through simulations and experiments, which enhances navigation stability by setting a maximum standby time and alternative point strategy. The experimental results show that the WW algorithm can prevent the robot from entering a false dead state. The paper concludes with suggestions for further optimization of the algorithm and the integration of more complex intelligent functions using deep learning.
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Cuevas Castañeda, Cristian Camilo. "Ros-gazebo. una valiosa Herramienta de Vanguardia para el Desarrollo de la Robótica." Publicaciones e Investigación 10 (March 22, 2016): 145. http://dx.doi.org/10.22490/25394088.1593.

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<p>El Sistema Operativo Robótico – ROS (de aquí en adelante ROS) representa un significativo avance en la tecnología robótica, ya que constituye un verdadero modelo colaborativo de desarrollo, abierto al público en general y con una gama de posibilidades aún por descubrir. ROS permite contar con estructuras ya diseña- das y programadas que luego se pueden modificar, evitando, de esta manera, comenzar de cero con cada diseño y superando la pérdida de tiempo inherente a la construcción de algoritmos de piezas comunes, como brazos y ruedas, entre otras. Tal plataforma se complementa con las herramientas de Rviz y Gazebo, que brindan simulaciones 3D del modelo robótico diseñado.</p>
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Akash, U., Naik Ananya, S. Sandeep, and Raj Deepti. "Autonomous Navigation with Collision Avoidance using ROS." Journal of Remote Sensing GIS & Technology 5, no. 2 (2019): 14–18. https://doi.org/10.5281/zenodo.2670059.

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<em>Simultaneous navigation and mapping is a modern mapping technique. The aim of SLAM is to develop 2D environment of a location while tracking the robot&rsquo;s position. This paper aims to develop ROS enabled robot with SLAM features in order to avoid collisions and navigate autonomously. A world is simulated using Gazebo and visualized using a tool called Rviz. Autonomous navigation is achieved by mapping the environment and plotting the odometry. Particle filtering is the algorithm on which SLAM works. This helps in using the odometry values to find the probable path for the robot to move whilst avoiding collision.</em> &nbsp;
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Alex, Amal. "Pipe Line Inspection Robot." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 766–72. http://dx.doi.org/10.22214/ijraset.2022.45392.

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Abstract: Pipelines are mainly constructed to transport all kinds of fluids and gases. Many accidents had occurred from fluid leaks because of cracks and corrosion of pipelines. To eliminate or minimize the accidents periodical inspection of pipelines should be done. Analysis and control of autonomous robot for pipeline inspection requires design and model of a robot equipped with the proper sensors. This project deals with the design, modelling (software) and simulation of pipeline inspection robot. The robot is an autonomous mobile robot. The robot is placed at the entrance of the pipe, by giving suitable commands it will move forward and inspect the defects inside the pipe. This inspection robot includes camera for visual inspection to identify the cracks and corrosion in pipe. It captures the inner images of the pipe for further investigation. This robot also includes a LIDAR sensor for mapping of the pipe. The mapping and navigation are all done in ROS (Robot Operating System) with help of Gazebo and Rviz. The design of the robot is created in Fusion 360 and then it is transferred into Gazebo. The simulation of robot is done in circular pipes.
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Parak, Roman, and Radomil Matousek. "Comparison of Multiple Reinforcement Learning and Deep Reinforcement Learning Methods for the Task Aimed at Achieving the Goal." MENDEL 27, no. 1 (2021): 1–8. http://dx.doi.org/10.13164/mendel.2021.1.001.

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Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) methods are a promising approach to solving complex tasks in the real world with physical robots. In this paper, we compare several reinforcement learning (Q-Learning, SARSA) and deep reinforcement learning (Deep Q-Network, Deep Sarsa) methods for a task aimed at achieving a specific goal using robotics arm UR3. The main optimization problem of this experiment is to find the best solution for each RL/DRL scenario and minimize the Euclidean distance accuracy error and smooth the resulting path by the Bézier spline method. The simulation and real word applications are controlled by the Robot Operating System (ROS). The learning environment is implemented using the OpenAI Gym library which uses the RVIZ simulation tool and the Gazebo 3D modeling tool for dynamics and kinematics.
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Sarah, Haider Abdulredah, and Jasim Kadhim Dheyaa. "Developing a real time navigation for mobile robots at unknown environments." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 500–509. https://doi.org/10.11591/ijeecs.v20.i1.pp500-509.

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Mobile robot needs to navigate at unknown environments and constructing its maps at the same time. Therefore, we proposed to use an algorithm named simultaneous localization and mapping (SLAM). Then, we suggested the extended kalman filter algorithm (EKF) to solve the SLAM problem which is implemented at different unknown environments containing a different number of landmarks where the detectable landmarks will play an important role in controlling the overall navigation process and on EKFSLAM technique&rsquo;s performance. MATLAB simulation results show that the performance of EKF-SLAM path is enhanced as the number of landmarks increased, so the performance becomes better as compared with an odometry path depending on the value of mean square error. After that, we simulated mobile robot platform named TurtleBot2e in Gazebo simulator to achieve the SLAM algorithm for different environments based on G-mapping algorithm which was built on robot operating system (ROS). The main contribution that comes with this work is the simulation of SLAM technique is done by using two different software platforms separately (MATLAB and ROS). Finally, the execution time to build a map is computed for each environment in Gazebo simulator, and we concluded that it is increased when the landmarks are increased.
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Conference papers on the topic "Gazebo and rviz"

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M J, Athul Krishna, Ajai V. Babu, Suraj Damodaran, Rekha K. James, Muhammed Murshid, and Tripti S. Warrier. "ROS2 - Powered Autonomous Navigation for TurtleBot3: Integrating Nav2 Stack in Gazebo, RViz and Real-World Environments." In 2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). IEEE, 2024. https://doi.org/10.1109/spices62143.2024.10779642.

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Raman, Adhiti T., Venkat N. Krovi, and Matthias J. A. Schmid. "Empowering Graduate Engineering Students With Proficiency in Autonomy." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-86316.

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A new class of distributed, autonomous systems is emerging, capable of exploiting multimodal distributed and networked spatial and temporal data (at significantly larger scales). A renaissance autonomy engineer requires proficiency in both traditional engineering concepts as well as a systems engineering skillset for implementing the ensuing complex systems. In this paper, we describe goals, development and first offering of a scaffolded course: “AuE 893 Autonomy: Science and Systems” to begin addressing this goal. Geared towards graduate engineering students, with limited prior exposure, the course complements the concepts from traditional courses (on mobile-robotics) with experiential hands-on system-integration efforts (building on the F1tenth.org kits). The staged course structure initially builds upon open-source Robotics Operating System (ROS) tutorials on simulated systems (Gazebo/RViz) with networked communication; Hardware-in-the-loop realization (with a Turtlebot platform) then aids the exploration (and reinforcement) of autonomy concepts. The course culminates in a final-project comprising performance testing with student-team integrated scaled Autonomous Remote Control cars (based on the F1tenth.org parts-list). All three student teams were successful in navigating around a closed racecourse at speeds of 10–15 miles per hour, using Simultaneous Localization and Mapping (SLAM) for situational awareness and obstacle-avoidance. We conclude with discussion of lessons-learnt and opportunities for future improvement.
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Journal articles
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