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1
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’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>
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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’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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Almusayli, Asma, Tanveer Zia, and Emad-ul-Haq Qazi. "Drone Forensics: An Innovative Approach to the Forensic Investigation of Drone Accidents Based on Digital Twin Technology." Technologies 12, no. 1 (2024): 11. http://dx.doi.org/10.3390/technologies12010011.
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In recent years, drones have become increasingly popular tools in criminal investigations, either as means of committing crimes or as tools to assist in investigations due to their capability to gather evidence and conduct surveillance, which has been effective. However, the increasing use of drones has also brought about new difficulties in the field of digital forensic investigation. This paper aims to contribute to the growing body of research on digital forensic investigations of drone accidents by proposing an innovative approach based on the use of digital twin technology to investigate drone accidents. The simulation is implemented as part of the digital twin solution using Robot Operating System (ROS) and simulated environments such as Gazebo and Rviz, demonstrating the potential of this technology to improve investigation accuracy and efficiency. This research work can contribute to the development of new and innovative investigation techniques.
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Soebhakti, Hendawan, and Robbi Hermawansya Pangantar. "Simulation of Mobile Robot Navigation System using Hector SLAM on ROS." JURNAL INTEGRASI 16, no. 1 (2024): 11–20. http://dx.doi.org/10.30871/ji.v16i1.5755.
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The ability to move from one point to the destination point autonomously is very necessary in AMR robots, to be able to meet this, the robot must be able to detect the surrounding environment and know its location to the environment, the Hector SLAM algorithm is added using the LIDAR sensor, and to find out the ability of the LIDAR sensor with the Hector SLAM and computer specifications in order to process properly, a simulation of the HECTOR SLAM with the LIDAR sensor was made. Simulation is carried out by creating an environment map on the Gazebo. Then explore environmental mapping using Hokuyo LIDAR which has been added to the turtlebot3 model waffle_pi to the simulated environment map. In this study, a model of the second floor lobby environment and Brail of the Batam State Polytechnic was used which was made in the form of a simulation on the Gazebo, where robots that have used LIDAR will be controlled with a keyboard around the simulation environment, where simultaneously the mapping and localization process runs and the process can be seen on the Rviz in real-time, where LIDAR will send data in the form of distance readings that will be received by Hector SLAM. The results of this study are expected that Hector SLAM using LIDAR sensor simulation can produce environmental mapping and localization in the simulation environment and obtain a minimum computer specification to process data from the SLAM Hector process using LIDAR sensors.
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Jawed Iqbal Alfaruqiy, Joko Endrasmono, and Zindhu maulana Ahmad Putra. "Implementasi Robot Operating System pada Robot ABU ROBOCON 2024 dengan Metode High Precision Path Planning." Jurnal Elektronika dan Otomasi Industri 11, no. 3 (2024): 736–47. http://dx.doi.org/10.33795/elkolind.v11i3.6313.
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Kontes Robot Indonesia (KRI) adalah kompetisi nasional dalam bidang robotika yang diselenggarakan oleh Pusat Prestasi Nasional, Kementerian Pendidikan dan Kebudayaan Republik Indonesia. Salah satu kategorinya, Kontes Robot ABU Indonesia (KRAI), mengikuti tema dan aturan dari ABU Robocon. Pada tahun 2024, tema "Harvest Day" mengharuskan robot bergerak otomatis tanpa intervensi manual, menjadikan lokalisasi yang akurat sangat penting untuk memastikan pergerakan robot yang tepat di arena. Lokalisasi, yang merupakan proses penentuan posisi robot, dilakukan menggunakan Robot Operating System (ROS), memanfaatkan pustaka seperti Gazebo dan RViz untuk meningkatkan akurasi dan pemantauan gerakan robot. Dengan Adaptive Monte Carlo Localization (AMCL) dan sensor RPLidarA1M8, robot dapat menentukan posisinya pada sumbu x dan y, serta arah gerak berikutnya. Penelitian ini menunjukkan bahwa AMCL dalam ROS efektif untuk lokalisasi dan perencanaan jalur dengan parameter yang dapat disesuaikan. Namun, untuk hasil optimal, diperlukan peningkatan desain perangkat keras, terutama pada swerve drive dan rotary encoder, serta komunikasi yang lebih efisien antara mikrokontroler STM32F407VGTx dan laptop agar perencanaan jalur lebih cepat dan efektif.
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Haider Abdulredah, Sarah, and Dheyaa Jasim Kadhim. "Developing a real time navigation for the mobile robots at unknown environments." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 500. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp500-509.
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<p><span>This research deals with the feasibility of a mobile robot to navigate and discover its location at unknown environments, and then constructing maps of these navigated environments for future usage. In this work, we proposed a modified Extended Kalman Filter- Simultaneous Localization and Mapping (EKF-SLAM) technique which was implemented for different unknown environments containing a different number of landmarks. Then, the detectable landmarks will play an important role in controlling the overall navigation process and EKF-SLAM technique’s performance. MATLAB simulation results of the EKF-SLAM technique come with better performance as compared with an odometry approach performance in terms of measuring the mean square error, especially when increasing the number of landmarks. After that, we simulate and evaluate a mobile robot platform named TurtleBot2e in Gazebo simulator software to achieve the using of the SLAM technique for a different environment using the Rviz library which was built on Robot Operating System in Linux. The main conclusion comes with this work is the simulation and implementation of the SLAM technique using two software platforms separately (MATLAB and ROS) in different unknown environments containing a different number of landmarks so a few number of landmark will make the mobile robot loses its path.</span></p>
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Lone, Mohd Aaqib, Szilveszter Kovács, and Owais Mujtaba Khanday. "Implementation Guidelines for Ethologically Inspired Fuzzy Behaviour-Based Systems." Infocommunications journal 16, no. 3 (2024): 43–56. http://dx.doi.org/10.36244/icj.2024.3.4.
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The adaptation of ethologically inspired behaviour models for human-machine interaction e.g. in Ethorobotics has become a challenging research topic in recent years. This paper presents a Fuzzy Behaviour Description Language (FBDL) approach for analyzing animal aggression behaviour. Fuzzy logic and fuzzy set theory approaches are used to analyze and classify the subjective impression of aggressive behaviour in a particular situation. This research aims to perform a meta analysis of aggression behaviour based on the fundamental values of animals and the possible ways of implementing animal aggressive behaviour in robots. Ultimately aiming to enhance the adaptability and effectiveness of human-robot interaction and performance in various real-world scenarios, e.g., by expressing disagreement in the direction of the human operator in case of unclear, or unsafe cooperative situations. In both industrial and everyday settings, mobile robots and robotic vehicles are becoming increasingly prevalent. Integrating aggressive behaviour into robotics is essential for boosting interactions between humans and robots, promoting safety in dynamic contexts, and getting a deeper understanding of animal behaviour. It aids robots in asserting their presence, maneuvering around barriers, and efficiently adjusting to dynamic surroundings. This guarantees more seamless operations in industrial and daily environments while also enhancing our comprehension of both robotics and ethology. We present graphical depictions of various animal behaviours, as well as trajectories, Gazebo simulations, and RViz visualizations of the animal robot, demonstrating the animal’s escape behaviour.
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Rahul S. Pol. "Optimizing Precision and Operational Efficiency in Object Manipulation: A Novel Algorithmic Paradigm for the UR-3 Robotic Arm Integrated with ROS Framework." Journal of Information Systems Engineering and Management 10, no. 23s (2025): 207–25. https://doi.org/10.52783/jisem.v10i23s.3697.
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This research presents a sophisticated algorithmic framework designed to optimize pick-and-place operations using the UR-3 robotic arm within the Robot Operating System (ROS) architecture. The study addresses key challenges in robotic manipulation, such as achieving high-precision 3D pose planning, real-time object localization, and singularity avoidance. By integrating ArUco marker-based object recognition, the proposed method enhances the robot’s ability to accurately detect and manipulate objects in dynamic and unstructured environments. A meticulous approach is employed to fine-tune the parameters of the Open Motion Planning Library (OMPL) within ROS’s MoveIt framework, improving path planning efficiency and object handling. The UR-3 robotic arm’s six degrees of freedom are leveraged to navigate complex spaces while avoiding obstacles and optimizing motion trajectories. Through advanced control strategies, the gripper system is calibrated to adapt to various object shapes, sizes, and weights, enhancing the overall reliability of the pick-and-place tasks. The algorithm also incorporates singularity detection and avoidance mechanisms, ensuring smooth and continuous motion during operations. Extensive experiments conducted in simulation environments such as Gazebo and RViz demonstrate significant improvements in both accuracy and speed. Performance metrics including path optimality, computational efficiency, and task completion rates were measured, validating the system's robustness. Results show a marked increase in task efficiency, with enhanced adaptability to diverse object configurations and real-world constraints. This research contributes to the field of robotic manipulation by providing a comprehensive solution to optimize automated pick-and-place operations, offering potential applications in industrial automation and intelligent manufacturing.
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Victor Nosakhare Oriakhi. "Autonomous Navigation for Differential Drive Robots: Grid-Based Fastslam with AMCL in ROS2." International Journal of Latest Technology in Engineering Management & Applied Science 14, no. 1 (2025): 147–78. https://doi.org/10.51583/ijltemas.2025.1401016.
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Abstract: The research aims to establish a robust autonomous navigation system for differential drive mobile robots, leveraging URDF modeling, SLAM techniques, AMCL, Gazebo simulation, and RViz2 visualization. The central objective is to enable robots to autonomously perceive their surroundings, construct accurate maps, self-localize, and navigate. The integration of URDF defines robot attributes, SLAM produces precise maps, and AMCL ensures reliable localization. Gazebo facilitates testing, while RViz2 provides real-time visualization. The outcome is an efficient navigation system empowering robots to independently navigate intricate environments. Beyond warehousing, applications span service robotics, exploration, and environmental monitoring. The research's significance lies in addressing a fundamental robotics challenge, advancing autonomous mobility across sectors. The approach's efficacy is validated through testing, with potential contributions to robotics research and real-world applications. achieved 97% mapping accuracy with a 5% deviation in simulated environments.
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Mohammad, Talal Ghazal, Al-Ghadhanfari Murtadha, and Zuhair Waisi Najwan. "Simulation of autonomous navigation of turtlebot robot system based on robot operating system." Bulletin of Electrical Engineering and Informatics (BEEI) 13, no. 2 (2024). https://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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-, Koushik Paul, Abhisri Dugar -, Abhishek Dugar -, Abhradeep Hazra -, and Aronno Ghosh -. "ROS-Driven Autonomous Navigation: A Scalable Framework for Wheeled Robots in Dynamic Environments." International Journal For Multidisciplinary Research 7, no. 1 (2025). https://doi.org/10.36948/ijfmr.2025.v07i01.35151.
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Autonomous navigation is important for wheeled robots in dynamic and unstructured environments. This paper details a ROS-based framework for motion planning that gives a robust and modular platform for the development and integration of robotic systems. The methodology involves robot modeling, environment mapping, path planning, control strategies, and execution mechanisms. It is built on ROS packages and tools like RViz, Gazebo, and MoveIt! It has to efficiently plan motion. This is simulated in details and even run in real-life. From both simulation and the real-world analysis, this shows that such a system would really navigate even constrained environments such as smooth trajectory generation or obstacle avoidance. This shows scalability and applicability of such systems, based on the ROS architecture, towards highly variant applications starting from industrial automation all the way up to an autonomous vehicle.
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Zick, Lucas Alexandre, Dieisson Martinelli, André Schneider de Oliveira, and Vivian Cremer Kalempa. "Teleoperation system for multiple robots with intuitive hand recognition interface." Scientific Reports 14, no. 1 (2024). https://doi.org/10.1038/s41598-024-80898-x.
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AbstractRobotic teleoperation is essential for hazardous environments where human safety is at risk. However, efficient and intuitive human–machine interaction for multi-robot systems remains challenging. This article aims to demonstrate a robotic teleoperation system, denominated AutoNav, centered around autonomous navigation and gesture commands interpreted through computer vision. The central focus is on recognizing the palm of the hand as a control interface to facilitate human–machine interaction in the context of multi-robots. The MediaPipe framework was integrated to implement gesture recognition from a USB camera. The system was developed using the Robot Operating System, employing a simulated environment that includes the Gazebo and RViz applications with multiple TurtleBot 3 robots. The main results show a reduction of approximately 50% in the execution time, coupled with an increase in free time during teleoperation, reaching up to 94% of the total execution time. Furthermore, there is a decrease in collisions. These results demonstrate the effectiveness and practicality of the robotic control algorithm, showcasing its promise in managing teleoperations across multi-robots. This study fills a knowledge gap by developing a hand gesture-based control interface for more efficient and safer multi-robot teleoperation. These findings enhance human–machine interaction in complex robotic operations. A video showing the system working is available at https://youtu.be/94S4nJ3IwUw.
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Munoz Ubando, Luis Alberto, Alexander Amigud, and Ekaterina Sirazitdinova. "Computer simulation and hands-on labs: A case study of teaching robotics and AI." International Journal of Mechanical Engineering Education, March 22, 2024. http://dx.doi.org/10.1177/03064190241240416.
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When teaching robotics, instructors face the challenge of finding an effective approach to bridge theoretical concepts and practical applications. Both computer simulations and hands-on laboratory experiments provide learners with opportunities for active, immersive, and experiential learning. As students progress from introductory to advanced topics and from theory to practice, their performance is contingent upon earlier knowledge and may increase, remain unchanged, or decrease. The question that arises is whether computer simulation can serve as a viable foundation for fostering an understanding of theory that enables the subsequent grasp of advanced practical concepts in robotics. Put another way, when students are introduced to the field of robotics through computer simulation, how will they perform when presented with advanced hands-on tasks involving the construction of physical robots to solve problems in physical space? To answer this question, we examined undergraduate student performance ( n = 107) across two robotics courses—an introductory course using computer simulation (Robot Operating System, Rviz, and GAZEBO) and an advanced course using physical hardware (Puzzlebot), leveraging the hardware's capability for AI tasks such as machine vision (Nvidia Jetson Nano development kit). Our findings suggest that student performance increased as they progressed from using computer simulation to engaging with hardware in the physical environment, further suggesting that teaching with computer simulations provides an adequate foundation to learn and complete more advanced tasks.
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Yao, Zhifeng, Fengxia Xu, and Chunsong Han. "Forecast-Island and Bidding A*-Euclidean Selecting Boustrophedon Coordination Algorithm for Exploration." International Journal of Pattern Recognition and Artificial Intelligence 35, no. 14 (2021). http://dx.doi.org/10.1142/s0218001421590485.
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Exploration algorithms based on the Boustrophedon path seldom consider the impacts of a robot turning at corners on the exploration time. This paper proposes the Forecast-Island and Bidding A*-Euclidean Selecting Boustrophedon Coordination (FIBA*ESBC) algorithm to calculate the turning time at corners in the overall exploration time and introduces a method to estimate the walking time in the Boustrophedon paths in order to determine the directions for path execution. Typically, in bidding-based exploration tasks, the cost is the Euclidean distance between the current position of the robot and the target point. When there is an obstacle between two points, the cost is set to infinity. Therefore, the selected target point is sometimes not optimal. The FIBA*ESBC algorithm is based on the exploration cost of a combination of the Euclidean distance and A* algorithm walking path, which can effectively solve this problem. Because the bidding is based on a greedy algorithm, the robot has a small unexplored island in the later exploration stage; therefore, full exploration is not possible or requires a long time with several repeated paths. The FIBA*ESBC algorithm prioritizes the exploration and estimation of hidden and existing unexplored islands. It can realize complete exploration and decrease the exploration time. Through simulation experiments conducted using Gazebo and RViz, the feasibility of the FIBA*ESBC algorithm is verified. Moreover, a simulation experiment is conducted in MATLAB for comparison with other algorithms. The analysis of the experimental data shows that the proposed algorithm has a relatively short exploration time.
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Jiang, FaGuang, Kebing Chen, Yang Chen, and Cheng Tian. "Trajectory planning and automatic docking of LNG five-axis loading arm." Engineering Computations, September 23, 2024. http://dx.doi.org/10.1108/ec-04-2024-0352.
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PurposeIn response to the challenges posed by the conventional manual flange docking method in the LNG (Liquefied Natural Gas) loading process, such as low positioning accuracy, constraints on production efficiency and safety hazards, this study analyzed the LNG five-axis loading arm’s main functions and structural characteristics.Design/methodology/approachAn automated solution for the joints of the LNG loading arm was designed. The forward kinematic model of the LNG loading arm was established using the Denavit–Hartenberg (D-H) parameter method, and its workspace was analyzed. The Newton–Raphson iteration method was employed to solve the inverse kinematics of the LNG loading arm, facilitating trajectory planning. The relationship between the target position and the joint variables was established to verify the stability of the arm’s motion. Flange center identification was achieved using the Hough transform function. Based on the ROS platform, combined with Gazebo and Rviz, an experimental simulation of automatic docking of the LNG loading arm was conducted.FindingsThe docking errors in the XYZ directions were all less than 0.8 mm, meeting the required docking accuracy. Moreover, the motion performance of the loading arm during docking was smooth and free of abrupt changes, validating its capability to accomplish the automatic docking task.Originality/valueThe proposed trajectory planning and automatic docking scheme can be used for the rapid filling of LNG filling arms and LNG tankers to improve the efficiency of LNG transportation. In guiding the docking, the proposed automatic docking scheme is an accurate and efficient way to improve safety.
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Sharma, Urvashi, Shalli Rani, and Mohammad Shabaz. "ACAMR: AI-Enabled Communication Algorithm in ROS to Improve Mobile Robot Connectivity in Internet of Robotic Things for AMVs." Journal of Intelligent & Robotic Systems 111, no. 2 (2025). https://doi.org/10.1007/s10846-025-02269-6.
Full textAbstract:
Abstract Autonomous Marine Vehicles (AMVs) are crucial in various maritime applications, including ocean exploration, environmental monitoring and offshore infrastructure inspections. Existing approaches to robot navigation such as reinforcement learning and supervised learning, often face challenges in dynamic environments due to high training overhead, poor generalization, and a lack of real-time adaptability. Federated learning techniques partially address privacy concerns but still incur significant communication costs and latency. Centralized architectures further suffer from bottlenecks and single-point failures, making them less suitable for mission-critical tasks. The framework integrates sensor fusion techniques, predictive analytics, and reinforcement learning-based FTC algorithms to enable AMVs to recover from propulsion, sensor, and communication failures. Furthermore, the incorporation of Internet of Robotic Things (IoRT) allows distributed fault monitoring, cooperative decision-making, and secure cloud-based diagnostics, enhancing the resilience of AMV fleets. The goal is to take advantage of both the ROS’s strengths in robotic control and coordination with the IoT’s strengths in connecting devices and exchanging data efficiently. In the following work, an integrated framework is proposed that allows ROS-enabled robots to communicate and work together using IoT protocols and the infrastructure of AMVs. Robots are able to learn from their environments, adapt to new situations, and complete collaborative tasks more effectively and independently. SLAM, Rviz and ROS2 have been discussed and validation of the proposed work over state of art approaches is shown for navigation of mobile robots in Gazebo software. Experimental results demonstrate a 34% reduction in navigation time, 29% improvement in distance efficiency, and 42% increase in task success rate compared to baseline learning models.