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Journal articles on the topic 'ROS/Gazebo'

Author: Grafiati
Published: 4 May 2024

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1

Agha, Rawan A. AlRashid, Zhwan Hani Mahdi, Muhammed N. Sefer, and Ibrahim Hamarash. "A ROS-Gazebo Interface for the Katana Robotic Arm Manipulation." UKH Journal of Science and Engineering 5, no. 1 (June 30, 2021): 26–37. http://dx.doi.org/10.25079/ukhjse.v5n1y2021.pp26-37.

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Nowadays, simulators are being used more and more during the development of robotic systems due to the efficiency of the development and testing processes of such applications. Undoubtedly, these simulators save time, resources and costs, as well as enable ease of demonstrations of the system. Specifically, tools like the open source Robotic Operating System (ROS) and Gazebo have gained popularity in building models of robotic systems. ROS is extensively used in robotics due to the pros of hardware abstraction and code reuse. The Gazebo platform is used for visualisation because of its high compatibility with ROS. In this paper, ROS and Gazebo have been integrated to build an interface for the visualisation of the Katana Arm manipulator.
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Kinouchi, Yusuke, Hiroyoshi Kojima, Tatsuya Hashimoto, Takayuki Ono, Takayuki Koyama, Kenji Hashimoto, and Atsuo Takanishi. "Robots utilizing ROS/Gazebo in Mitsubishi Heavy Industries." Journal of the Robotics Society of Japan 35, no. 4 (2017): 276–79. http://dx.doi.org/10.7210/jrsj.35.276.

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Rivera, Zandra B., Marco C. De Simone, and Domenico Guida. "Unmanned Ground Vehicle Modelling in Gazebo/ROS-Based Environments." Machines 7, no. 2 (June 14, 2019): 42. http://dx.doi.org/10.3390/machines7020042.

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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.
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Andrean, Danu, and Nuryono Satya Widodo. "Simulation and Implementation of RSCUAD Walking Robot Based on ROS and Gazebo Simulator." Control Systems and Optimization Letters 1, no. 2 (July 18, 2023): 93–98. http://dx.doi.org/10.59247/csol.v1i2.32.

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This research describes the virtual humanoid robot R-SCUAD using the Gazebo simulator. In its development, humanoid robots often perform movements that have a negative impact on the robot's hardware, therefore the development of a virtual robot model is a solution to overcome this problem. So that the robot can be simulated before running. Gazebo is a robot simulator that allows to accurately simulate, design and test robots in various environments. Gazebo itself is a simulation used by ROS (robotic operating system). The simulation is built by doing a 3D design process in solidwork software and exported to a URDF file that matches the format on the ROS. Tests carried out on robots are by comparing virtual robots with real robots. From the tests carried out on the robot, it was found that the virtual robot can walk according to the real robot, such as falling if the robot's condition is not balanced. The simulation robot also moves according to the real robot when the controls are carried out.
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Pineda Torres, Franklin, and Luis Alejandro Arias Barragán. "PRM navigation in trading drone and Gazebo simulation." Visión electrónica 14, no. 1 (January 31, 2020): 43–50. http://dx.doi.org/10.14483/22484728.16494.

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Starting from a commercial drone AR Dron Parrot 2.0, an autonomous navigation process is developed with a PRM probabilistic route planner in real time, through a ROS network between the drone and the Gazebo simulation software. Using the robotics system toolbox from software Matlab that interacts with Gazebo, it is possible to study the desired trajectory planner, in addition, the creation and connection of the ROS network on the Linux operating system, where the navigation algorithm is analyzed from the practical vs., simulation points of views. The errors that are presented are minimal, taking into account the propagation delays and the control algorithm; this is in charge of receiving location information in order to correct and minimized the mean square error.
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Uslu, Erkan, Furkan Çakmak, Nihal Altuntaş, Salih Marangoz, Mehmet Fatih Amasyalı, and Sırma Yavuz. "An architecture for multi-robot localization and mapping in the Gazebo/Robot Operating System simulation environment." SIMULATION 93, no. 9 (June 6, 2017): 771–80. http://dx.doi.org/10.1177/0037549717710098.

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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.
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7

Vujić, Đorđe. "SIMULACIJA ROBOTA BAZIRANOG NA DIFERENCIJALNOM POGONU KORIŠĆENJEM GAZEBO SIMULATORA I ROS-A." Zbornik radova Fakulteta tehničkih nauka u Novom Sadu 37, no. 04 (April 8, 2022): 661–65. http://dx.doi.org/10.24867/17be17vujic.

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U ovom radu razvijen je simulator robota sa diferencijalnim pogonom, uz oslonac na Gazebo platformu za simulaciju robotskih aplikacija i robotski operativnisistem (ROS). Razvijenim simulatorom se posebno razmatra sprega ova dva alata. Analiza rezultata vršena je korišćenjem raznih alata kompatibilnih sa robotskim operativnim sistemom.
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8

Jalil, Abdul. "ROBOT OPERATING SYSTEM (ROS) DAN GAZEBO SEBAGAI MEDIA PEMBELAJARAN ROBOT INTERAKTIF." ILKOM Jurnal Ilmiah 10, no. 3 (December 20, 2018): 284–89. http://dx.doi.org/10.33096/ilkom.v10i3.365.284-289.

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Shimchik, Ilya, Artur Sagitov, Ilya Afanasyev, Fumitoshi Matsuno, and Evgeni Magid. "Golf cart prototype development and navigation simulation using ROS and Gazebo." MATEC Web of Conferences 75 (2016): 09005. http://dx.doi.org/10.1051/matecconf/20167509005.

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Московский, А. Д., and М. А. Ровбо. "РАЗРАБОТКА СРЕДСТВ ОБУЧЕНИЯ РЕАЛИСТИЧНЫХ МОДЕЛЕЙ МОБИЛЬНЫХ РОБОТОВ В СИМУЛЯТОРЕ ROS GAZEBO." Вестник Военного инновационного технополиса «ЭРА» 3, no. 2 (2022): 175–81. http://dx.doi.org/10.56304/s2782375x22020127.

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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 (April 1, 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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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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13

Huang, Peihao, Yong Tang, Bingsan Yang, and Tao Wang. "Research on Scenario Modeling for V-Tail Fixed-Wing UAV Dynamic Obstacle Avoidance." Drones 7, no. 10 (September 25, 2023): 601. http://dx.doi.org/10.3390/drones7100601.

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With the advantages of long-range flight and high payload capacity, large fixed-wing UAVs are often used in anti-terrorism missions, disaster surveillance, and emergency supply delivery. In the existing research, there is little research on the 3D model design of the V-tail fixed-wing UAV and 3D flight environment modeling. The study focuses on designing a comprehensive simulation environment using Gazebo and ROS, referencing existing large fixed-wing UAVs, to design a V-tail aircraft, incorporating realistic aircraft dynamics, aerodynamics, and flight controls. Additionally, we present a simulation environment modeling approach tailored for obstacle avoidance in no-fly zones, and have created a 3D flight environment in Gazebo, generating a large-scale terrain map based on the original grayscale heightmap. This terrain map is used to simulate potential mountainous terrain threats that a fixed-wing UAV might encounter during mission execution. We have also introduced wind disturbances and other specific no-fly zones. We integrated the V-tail fixed-wing aircraft model into the 3D flight environment in Gazebo and designed PID controllers to stabilize the aircraft’s flight attitude.
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Lipoviy, Dmitriy A., and Aleksandr S. Maltsev. "Development Quadcopter Flight Controller Architecture Based on a Single-Board Computer Raspberry Pi." Vestnik NSU. Series: Information Technologies 18, no. 3 (2020): 19–33. http://dx.doi.org/10.25205/1818-7900-2020-18-3-19-33.

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This work is devoted to development of a modular flight controller architecture for a quadcopter. The hardware part of the controller is a single-Board raspberry Pi computer, for developing the software part Robot Operating System (ROS) framework was used. The paper describes the developed architecture, control algorithms, results of flight experiments in the Gazebo physical simulator.
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Zhang, Bowei, and Pengcheng Liu. "Control and benchmarking of a 7-DOF robotic arm using Gazebo and ROS." PeerJ Computer Science 7 (March 23, 2021): e383. http://dx.doi.org/10.7717/peerj-cs.383.

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The robot controller plays an important role in controlling the robot. The controller mainly aims to eliminate or suppress the influence of uncertain factors on the control robot. Furthermore, there are many types of controllers, and different types of controllers have different features. To explore the differences between controllers of the same category, this article studies some controllers from basic controllers and advanced controllers. This article conducts the benchmarking of the selected controller through pre-set tests. The test task is the most commonly used pick and place. Furthermore, to complete the robustness test, a task of external force interference is also set to observe whether the controller can control the robot arm to return to a normal state. Subsequently, the accuracy, control efficiency, jitter and robustness of the robot arm controlled by the controller are analyzed by comparing the Position and Effort data. Finally, some future works of the benchmarking and reasonable improvement methods are discussed.
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Lavrenov, Lavrenov, Evgeni Magid, Matsuno Fumitoshi, Mikhail Svinin, and Jackrit Suthakorn. "Development and Implementation of Spline-based Path Planning Algorithm in ROS/Gazebo Environment." SPIIRAS Proceedings 18, no. 1 (February 21, 2019): 57–84. http://dx.doi.org/10.15622/sp.18.1.57-84.

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Path planning for autonomous mobile robots is an important task within robotics field. It is common to use one of the two classical approaches in path planning: a global approach when an entire map of a working environment is available for a robot or local methods, which require the robot to detect obstacles with a variety of onboard sensors as the robot traverses the environment. In our previous work, a multi-criteria spline algorithm prototype for a global path construction was developed and tested in Matlab environment. The algorithm used the Voronoi graph for computing an initial path that serves as a starting point of the iterative method. This approach allowed finding a path in all map configurations whenever the path existed. During the iterative search, a cost function with a number of different criteria and associated weights was guiding further path optimization. A potential field method was used to implement some of the criteria. This paper describes an implementation of a modified spline-based algorithm that could be used with real autonomous mobile robots. Equations of the characteristic criteria of a path optimality were further modified. The obstacle map was previously presented as intersections of a finite number of circles with various radii. However, in real world environments, obstacles’ data is a dynamically changing probability map that could be based on an occupancy grid. Moreover, the robot is no longer a geometric point. To implement the spline algorithm and further use it with real robots, the source code of the Matlab environment prototype was transferred into C++ programming language. The testing of the method and the multi criteria cost function optimality was carried out in ROS/Gazebo environment, which recently has become a standard for programming and modeling robotic devices and algorithms. The resulting spline-based path planning algorithm could be used on any real robot, which is equipped with a laser rangefinder. The algorithm operates in real time and the influence of the objective function criteria parameters are available for dynamic tuning during a robot motion.
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Dobrokvashina, Alexandra, Shifa Sulaiman, Timur Gamberov, Kuo-Hsien Hsia, and Evgeni Magid. "New Features Implementation for Servosila Engineer Model in Gazebo Simulator for ROS Noetic." Proceedings of International Conference on Artificial Life and Robotics 28 (February 9, 2023): 153–56. http://dx.doi.org/10.5954/icarob.2023.os6-3.

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18

Lilansa, Noval, Muhammad Nursyam Rizal, Pipit Anggraeni, and Nur Jamiludin Ramadhan. "Implementation consensus algorithm and leader-follower of multi-robot system formation." SINERGI 27, no. 1 (January 13, 2023): 45. http://dx.doi.org/10.22441/sinergi.2023.1.006.

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Robot technology has recently been applied to many applications to help human activities. Mobile Robot is one of the most flexible robot technology. This research uses a mobile robot designed using an omnidirectional wheel for the movement mechanism. Coordination and control of multi-robots can be assigned to perform any task from a different kind of field. Therefore, this paper aims to develop a multi-robot system to form a formation to do the task. The multi-robot system consists of three units Mobile Robot. The formation system will be built based on a coordinate point determined by a consensus point. The leader-follower topology is used to determine the orientation of the robot. ROS (Robot Operating System) is used as middleware to create a multi-robot system. The Open Base package in Gazebo Simulator is also used to simulate the movement of the multi-robot. From three test scenarios, this research results show that all the robots can do and follow the tasks simulated in the Gazebo with an average accuracy of 88.14%. Furthermore, no feedback from the robot to the Gazebo Simulator affects the robot's accuracy average below 90%.
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Cui, Jiaqi. "Path Planning and Parameter Adjustment." Academic Journal of Science and Technology 4, no. 3 (February 8, 2023): 187–90. http://dx.doi.org/10.54097/ajst.v4i3.5056.

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By building ros and gazebo simulation environment in virtual machine, path planning, navigation, positioning and SLAM mapping were carried out for Ackerman chassis car, and traffic sign recognition and lane line detection were completed by combining machine vision, so as to control the intelligent car's turning. Test the navigation parameters with TEB plug-in to obtain the best parameters. After testing, the navigation effect is ideal, with reliability and accuracy.
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Pietrzik, S., and B. Chandrasekaran. "Setting up and Using ROS-Kinetic and Gazebo for Educational Robotic Projects and Learning." Journal of Physics: Conference Series 1207 (April 2019): 012019. http://dx.doi.org/10.1088/1742-6596/1207/1/012019.

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21

Li, Lianjun, Yizhe Zhang, Michael Ripperger, Jorge Nicho, Malathi Veeraraghavan, and Andrea Fumagalli. "Autonomous Object Pick-and-Sort Procedure for Industrial Robotics Application." International Journal of Semantic Computing 13, no. 02 (June 2019): 161–83. http://dx.doi.org/10.1142/s1793351x19400075.

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This paper describes an industrial robotics application, named Gilbreth, for autonomously picking up objects of different types from a moving conveyor belt and sorting the objects into bins according to their type. The environment, which consists of a moving conveyor belt, a break beam sensor, a 3D camera Kinect sensor, a UR10 industrial robot arm with a vacuum gripper, and different object types such as pulleys, disks, gears, and piston rods, is inspired by the NIST ARIAC competition. A first version of the Gilbreth application is implemented leveraging a number of Robot Operating System (ROS) and ROS-Industrial (ROS-I) packages. The Gazebo package is used to simulate the environment, and six external ROS nodes have been implemented to execute the required functions. Experimental measurements of CPU usage and processing times of the ROS nodes are discussed. In particular, the object recognition ROS package requires the highest processing times and offers an opportunity for designing an iterative method with the aim to fasten completion time. Its processing time is found to be on par with the time required by the robot arm to execute its movement between four poses: pick approach, pick, pick retreat and place.
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Saipullah, Khairul Muzzammil, Wira Hidayat Mohd Saad, Sook Hui Chong, Muhammad Idzdihar Idris, and Syafeeza Ahmad Radzi. "ROS 2 Configuration for Delta Robot Arm Kinematic Motion and Stereo Camera Visualization." Journal of Robotics and Control (JRC) 3, no. 3 (May 1, 2022): 320–27. http://dx.doi.org/10.18196/jrc.v3i3.14436.

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The Delta robot is one of the robot types that is used in agriculture and industrial application. However, before the complex physical development of the robot, a simulation needs to be developed to ensure the perfect functionality of the design. Therefore, this paper presented a development of simulation for a parallel delta robot using a Robot Operating System 2 (ROS 2) environment and stereo camera visualization. The contribution of this research is to present the development details and the proposed solution to solve issues encountered during the development. The development of script in the format of eXtensible Markup Language (XML), Unified Robot Description Format (URDF), and Simulation Description Format (SDF) are presented for describing a robot's physical structure, allowing a robotic system to be depicted in a tree structure, and defining the delta robot arm, which is made up of closed-loop kinematic chain linkage that will be simulated in Gazebo. For the results, several Gazebo plugin libraries are compared and tested for the wheels motion control, stereo camera visualization, and delta robot arm kinematic motion. From the experiment, the best method is inverse kinematic motion the method is selected and used in the simulation. The selected method resulted in an average percentage error of 3.92%, 3.72%, and 2.92%, respectively for each joint.
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Pan, Qiran. "Design and analysis of an autonomous warehouse robot system with 6-DOF manipulator." Applied and Computational Engineering 34, no. 1 (January 22, 2024): 114–21. http://dx.doi.org/10.54254/2755-2721/34/20230310.

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With the increasing need for efficiency and accuracy in warehouse operations, the functions and market demands of automated warehouse robots are constantly increasing. This study presents the design, simulation, and implementation of a warehouse robot, showcasing effective automation solution. Leveraging the Robot Operating System (ROS) and Gazebo, a robot with a six-degree-of-freedom robotic arm for diverse manipulation tasks and a differential drive base for broad-spectrum navigation was designed. The simulation environment in Gazebo faithfully replicates real-world warehouse conditions, enabling comprehensive path planning and real-time modifications, powered by move_base. A camera sensor serves as the robot's safety system, designed to detect moving obstacles and initiate appropriate responses, contributing to the enhancement of warehouse safety standards. Simulation results demonstrate the robot's effectiveness in performing pick-and-place tasks while successfully navigating through the environment, indicating the significant potential for real-world warehouse automation applications. Therefore, this work provides a foundation reference for future research aimed at optimizing and expanding the capabilities of autonomous warehouse robots.
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Lizzio, Fausto Francesco, Stefano Primatesta, Haoyu Guo, and Giorgio Guglieri. "Design and SITL Performance of an online Distributed Target Estimation for UAV Swarm." Journal of Physics: Conference Series 2526, no. 1 (June 1, 2023): 012086. http://dx.doi.org/10.1088/1742-6596/2526/1/012086.

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Abstract In this paper, an online distributed target estimation for a swarm of Unmanned Aerial Vehicles in formation flight is considered. In particular, a flocking algorithm is employed to handle the formation task, while a Distributed Kalman Filter in the Information form is used to perform the estimate of a mobile target state. Software-In-The-Loop simulations are carried on in the ROS/Gazebo environment to illustrate the coupling between the formation and the estimation tasks. Moreover, the influence of swarm connectivity in the estimation process is analyzed, showing the benefits of information fusion.
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Anusha, Chilupuri. "Design of Single Legged Hopper Robot in Gazebo." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 3752–59. http://dx.doi.org/10.22214/ijraset.2023.54189.

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Abstract: Robotics is one of the core areas where the bioinspiration is frequently used to design various engineered morphologies to develop novel behavioral controllers comparable to human being and animals. Research on legged robots has been going on for over a century. The reason behind such sustained interest in legged robots is due to the fact that most of the earth’s land surface is inaccessible to wheeled or tracked systems. Legged animals can, however, be found everywhere. Thus, mankind has been fascinated with the idea of a mobile legged robot that can handle difficult terrain and be useful in the fields of transportation, forestry, agriculture, fire, fighting, hazardous areas, defense (carrying weapons to soldiers, de-mining), police purposes, assistive devices for walking, entertainment (toy production), robotic pets, and ocean and space exploration. Our project is based on one such example, single legged hopper robot a legged locomotive inspired by the jumping mechanism of click beetles. It aims to create a single legged hopper robot in Gazebo. A simulation tool that offers the ability to accurately, and efficiently, simulate populations of robots in complex indoor and outdoor environments. The Robot Operating System (ROS) works best in the LINUX OS, is the software of version Melodic + Gazebo 9, is utilized in the project.
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Sánchez, Manuel, Jesús Morales, Jorge L. Martínez, J. J. Fernández-Lozano, and Alfonso García-Cerezo. "Automatically Annotated Dataset of a Ground Mobile Robot in Natural Environments via Gazebo Simulations." Sensors 22, no. 15 (July 26, 2022): 5599. http://dx.doi.org/10.3390/s22155599.

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This paper presents a new synthetic dataset obtained from Gazebo simulations of an Unmanned Ground Vehicle (UGV) moving on different natural environments. To this end, a Husky mobile robot equipped with a tridimensional (3D) Light Detection and Ranging (LiDAR) sensor, a stereo camera, a Global Navigation Satellite System (GNSS) receiver, an Inertial Measurement Unit (IMU) and wheel tachometers has followed several paths using the Robot Operating System (ROS). Both points from LiDAR scans and pixels from camera images, have been automatically labeled into their corresponding object class. For this purpose, unique reflectivity values and flat colors have been assigned to each object present in the modeled environments. As a result, a public dataset, which also includes 3D pose ground-truth, is provided as ROS bag files and as human-readable data. Potential applications include supervised learning and benchmarking for UGV navigation on natural environments. Moreover, to allow researchers to easily modify the dataset or to directly use the simulations, the required code has also been released.
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Mattila, Joel, Riku Ala-Laurinaho, Juuso Autiosalo, Pauli Salminen, and Kari Tammi. "Using Digital Twin Documents to Control a Smart Factory: Simulation Approach with ROS, Gazebo, and Twinbase." Machines 10, no. 4 (March 23, 2022): 225. http://dx.doi.org/10.3390/machines10040225.

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Digital twin documents are expected to form a global network of digital twins, a “Digital Twin Web”, that allows the discovery and linking of digital twins with an approach similar to the World Wide Web. Digital twin documents can be used to describe various aspects of machines and their twins, such as physical properties, nameplate information, and communication interfaces. Digital twin is also one of the core concepts of the fourth industrial revolution, aiming to make factories more efficient through optimized control methods and seamless information flow, rendering them “smart factories”. In this paper, we investigate how to utilize digital twin documents in smart factory communication. We implemented a proof-of-concept simulation model of a smart factory that allowed simulating three different control methods: centralized client-server, decentralized client-server, and decentralized peer-to-peer. Digital twin documents were used to store the necessary information for these control methods. We used Twinbase, an open-source server software, to host the digital twin documents. Our analysis showed that decentralized peer-to-peer control was most suitable for a smart factory because it allowed implementing the most advanced cooperation between machines while still being scalable. The utilization of Twinbase allowed straightforward removal, addition, and modification of entities in the factory.
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Martínez, Jorge L., Jesús Morales, Manuel Sánchez, Mariano Morán, Antonio J. Reina, and J. Jesús Fernández-Lozano. "Reactive Navigation on Natural Environments by Continuous Classification of Ground Traversability." Sensors 20, no. 22 (November 10, 2020): 6423. http://dx.doi.org/10.3390/s20226423.

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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.
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Alex, Amal. "Pipe Line Inspection Robot." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 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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Teoh, Sean W. H., Kamarulzaman Kamarudin, Nasr A. N. Ali, Muhammad M. M. Zainal, Mohd R. Manan, and Syed M. Mamduh. "Reinforcement Learning for Mobile Robot’s Environment Exploration." Journal of Physics: Conference Series 2641, no. 1 (November 1, 2023): 012003. http://dx.doi.org/10.1088/1742-6596/2641/1/012003.

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Abstract Mobile robots are being are being applied in various industries to perform repetitive or dangerous tasks for humans to carry out. Autonomous mobile robots are more capable than automated guided vehicles (AGV) due to their ability to be adaptable to their environment which is important for exploration of unknown environments. It is difficult to program autonomous mobile robots to adapt to various situations it may face, thus machine learning can be applied to allow a mobile robot to learn how to navigate through environments by itself. Reinforcement learning is applied in this project so that a differential drive mobile robot can learn how to navigate through its environment while avoiding collision with surrounding walls and obstacles. The reinforcement learning process is simulated by using the Robot Operating System (ROS) and its simulator Gazebo. Controlled simulation environments are created using Gazebo for the purposes of training and performance testing. Simultaneous Localization and Mapping (SLAM) will be applied to generate a map of the environment. At the end of this project, the Turtlebot3 is able to map smaller controlled environments ranging between 18m2 to 27m2 without colliding with the surrounding walls.
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Dirgantara, Mohammad Ryan, Eka Budiarto, and Rusman Rusyadi. "3D Path Planning for Quadrotor Using Gazebo Simulator." ICONIET PROCEEDING 2, no. 3 (February 13, 2019): 200–205. http://dx.doi.org/10.33555/iconiet.v2i3.33.

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This research explores the 3D path planning for quadrotor, which is an unmannedaerial vehicle (UAV) with four rotors. The quadrotor is simulated using robot operating system(ROS) and Gazebo software, and is equipped with camera, GPS sensor, and inertialmeasurement unit (IMU) sensor to do the mapping of its environment. The packages used inROS are Hector Quadrotor package, joystick package, Octomap package, and MoveIt package.These packages were modified so that it could be integrated with each other and fulfill theobjective of this research. For the 3D path planning, a method called rapidly-exploring randomtree (RRT) is explored and implemented. Several experiments regarding the behavior of thequadrotor, the mapping, and the path planning were conducted to find out the performance andlimitations of the simulation. This simulation is set up so that it can be used to validate a newdesign of quadrotor before it is tested with a physical prototype.
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Heilig, Alexander, Ilshat Mamaev, Björn Hein, and Dmitrii Malov. "Adaptive particle filter for localization problem in service robotics." MATEC Web of Conferences 161 (2018): 01004. http://dx.doi.org/10.1051/matecconf/201816101004.

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In this paper we present a statistical approach to the likelihood computation and adaptive resampling algorithm for particle filters using low cost ultrasonic sensors in the context of service robotics. This increases the efficiency of the particle filter in the Monte Carlo Localization problem by means of preventing sample impoverishment and ensuring it converges towards the most likely particle and simultaneously keeping less likely ones by systematic resampling. Proposed algorithms were developed in the ROS framework, simulation was done in Gazebo environment. Experiments using a differential drive mobile platform with 4 ultrasonic sensors in the office environment show that our approach provides strong improvement over particle filters with fixed sample sizes.
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33

Hường, Trần Thị. "NGHIÊN CỨU ĐIỀU KHIỂN ROBOT TỰ HÀNH ỨNG DỤNG CHO ĐIỀU HƯỚNG THÔNG MINH TRÊN CƠ SỞ THUẬT TOÁN Q-LEARNING." TNU Journal of Science and Technology 227, no. 08 (May 19, 2022): 291–300. http://dx.doi.org/10.34238/tnu-jst.5745.

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Bài báo trình bày nghiên cứu về vấn đề điều khiển robot tự hành ứng dụng trong công nghiệp, trong dân dụng,v.v... để điều hướng thông minh trong môi trường không xác định trên cơ sở thuật toán Q-Learning. Công cụ lập trình là hệ điều hành cho robot ROS (Robot Operating System) và thực hiện điều hướng thông minh tự động cho robot với quá trình định vị robot trong môi trường phẳng và lập bản đồ hóa (gọi là SLAM - Simultaneous Localization and Mapping). Các kết quả nghiên cứu sử dụng công cụ lập trình ROS, trong môi trường Gazebo. Các thông tin được cập nhật từ bản đồ, môi trường hoạt động, vị trí điều khiển của robot và vật cản để tính toán quỹ đạo cho robot trong hệ thống điều hướng tự động. Mục tiêu nhằm tránh các chướng ngại vật một cách an toàn mà không gặp bất kỳ trở ngại nào trên đường đi.
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Adiuku, Ndidiamaka, Nicolas P. Avdelidis, Gilbert Tang, Angelos Plastropoulos, and Yanis Diallo. "Mobile Robot Obstacle Detection and Avoidance with NAV-YOLO." International Journal of Mechanical Engineering and Robotics Research 13, no. 2 (2024): 219–26. http://dx.doi.org/10.18178/ijmerr.13.2.219-226.

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Intelligent robotics is gaining significance in Maintenance, Repair, and Overhaul (MRO) hangar operations, where mobile robots navigate complex and dynamic environments for Aircraft visual inspection. Aircraft hangars are usually busy and changing, with objects of varying shapes and sizes presenting harsh obstacles and conditions that can lead to potential collisions and safety hazards. This makes Obstacle detection and avoidance critical for safe and efficient robot navigation tasks. Conventional methods have been applied with computational issues, while learning-based approaches are limited in detection accuracy. This paper proposes a vision-based navigation model that integrates a pre-trained Yolov5 object detection model into a Robot Operating System (ROS) navigation stack to optimise obstacle detection and avoidance in a complex environment. The experiment is validated and evaluated in ROS-Gazebo simulation and turtlebot3 waffle-pi robot platform. The results showed that the robot can increasingly detect and avoid obstacles without colliding while navigating through different checkpoints to the target location.
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35

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 (April 1, 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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36

Rajapaksha, U. U. Samantha, Chandimal Jayawardena, and Bruce A. MacDonald. "Design, Implementation, and Performance Evaluation of a Web-Based Multiple Robot Control System." Journal of Robotics 2022 (May 30, 2022): 1–24. http://dx.doi.org/10.1155/2022/9289625.

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Heterogeneous multiple robots are currently being used in smart homes and industries for different purposes. The authors have developed the Web interface to control and interact with multiple robots with autonomous robot registration. The autonomous robot registration engine (RRE) was developed to register all robots with relevant ROS topics. The ROS topic identification algorithm was developed to identify the relevant ROS topics for the publication and the subscription. The Gazebo simulator spawns all robots to interact with a user. The initial experiments were conducted with simple instructions and then changed to manage multiple instructions using a state transition diagram. The number of robots was increased to evaluate the system’s performance by measuring the robots’ start and stop response time. The authors have conducted experiments to work with the semantic interpretation from the user instruction. The mathematical equations for the delay in response time have been derived by considering each experiment’s input given and system characteristics. The Big O representation is used to analyze the running time complexity of algorithms developed. The experiment result indicated that the autonomous robot registration was successful, and the communication performance through the Web decreased gradually with the number of robots registered.
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37

Tinoco, Vítor, Benedita Malheiro, and Manuel F. Silva. "Design, Modeling, and Simulation of a Wing Sail Land Yacht." Applied Sciences 11, no. 6 (March 19, 2021): 2760. http://dx.doi.org/10.3390/app11062760.

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Autonomous land yachts can play a major role in the context of environmental monitoring, namely, in open, flat, windy regions, such as iced planes or sandy shorelines. This work addresses the design, modeling, and simulation of a land yacht probe equipped with a rigid free-rotating wing sail and tail flap. The wing was designed with a symmetrical airfoil and dimensions to provide the necessary thrust to displace the vehicle. Specifically, it proposes a novel design and simulation method for free rotating wing sail autonomous land yachts. The simulation relies on the Gazebo simulator together with the Robotic Operating System (ROS) middleware. It uses a modified Gazebo aerodynamics plugin to generate the lift and drag forces and the yawing moment, two newly created plugins, one to act as a wind sensor and the other to set the wing flap angular position, and the 3D model of the land yacht created with Fusion 360. The wing sail aligns automatically to the wind direction and can be set to any given angle of attack, stabilizing after a few seconds. Finally, the obtained polar diagram characterizes the expected sailing performance of the land yacht. The described method can be adopted to evaluate different wing sail configurations, as well as control techniques, for autonomous land yachts.
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38

Amiri, Mohammad Soleimani, Rizauddin Ramli, Mohd Aizat Ahmad Tarmizi, Mohd Faisal Ibrahim, and Khashayar Danesh Narooei. "Simulation and Control of a Six Degree of Freedom Lower Limb Exoskeleton." Jurnal Kejuruteraan 32, no. 2 (May 30, 2020): 197–204. http://dx.doi.org/10.17576/jkukm-2020-32(2)-03.

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In this paper, the development of controlling a six Degree of Freedom (DOF) Lower Limb Exoskeleton (LLE) model using the Robot Operating System (ROS) is presented. Moreover, this work proposes a method to analyze kinematic properties and control of the LLE before the prototype. The model of the LLE is described using Extensible Markup Language (XML) programming in the Unified Robot Description Format (URDF). The dynamic equation of the six-DoF LLE is determined by using Newton-Euler. In addition, a Proposition-Integral-Derivative (PID) controller is established in a feedback closed-loop control system. The PID controller is tuned via Ziegler-Nichols (Z-N). The tuned PID controller is tested in the Gazebo environment to confirm the performance of the proposed method. The nodes and topics flow chart of the programmed 3-D model of the LLE is described. Furthermore, a desired angular trajectory based on the phase on walking is defined for each joint of the LLE. The result shows that the actual pursue the desired angular trajectory for each joint. The average and maximum error of the angular trajectories for all the joints are less than 0.05 radian. It can be ascertained that our developed LLE model in the Gazebo simulator can be used for giving an overview of the walking pattern.
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39

Patrinopoulou, Niki, Ioannis Daramouskas, Dimitrios Meimetis, Vaios Lappas, and Vassilios Kostopoulos. "A Multi-Agent System Using Decentralized Decision-Making Techniques for Area Surveillance and Intruder Monitoring." Drones 6, no. 11 (November 16, 2022): 357. http://dx.doi.org/10.3390/drones6110357.

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A decentralized swarm of quadcopters designed for monitoring an open area and detecting intruders is proposed. The system is designed to be scalable and robust. The most important aspect of the system is the swarm intelligent decision-making process that was developed. The rest of the algorithms essential for the system to be completed are also described. The designed algorithms were developed using ROS and tested with SITL simulations in the GAZEBO environment. The proposed approach was tested against two other similar surveilling swarms and one approach using static cameras. The addition of the real-time decision-making capability offers the swarm a clear advantage over similar systems, as depicted in the simulation results.
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40

Ye, Zewei, Liwei Zeng, Qingzhang Wang, Zhicheng Wang, Jianhua Cui, Yanning Cao, Lingling Shen, and Xiaojun Qian. "An Intelligent Equipment Inspection System based on Robotic Arm." Frontiers in Computing and Intelligent Systems 6, no. 1 (November 30, 2023): 115–19. http://dx.doi.org/10.54097/fcis.v6i1.22.

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Intelligent robots have replaced manual labor to complete a lot of complicated work, especially in equipment inspection has advantages. The purpose of this paper is to design an intelligent robotic inspection system. The system is based on ROS and adopts YOLO algorithm, RRT* algorithm, Agile_grasp function package, and MoveIt! framework to realize the basic target recognition, position recognition, robotic arm navigation planning and other functions. The human-computer interaction interface has been designed, and the simulation test in Gazebo and the real machine test in real environment have also been completed. The system still has a large space for improvement, and further research will be conducted in the future in terms of response speed and accuracy.
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41

Trần, Lê Trung Chánh, Quang Hiếu Ngô, Tùng Khánh Nguyễn, Chiêu Linh Mai, Phước Lộc Trần, and Văn Lẻ Lê. "Mô phỏng khảo sát chất lượng của hệ thống tìm đường và bám đường cho xe tự hành lái bằng nguyên lý ackermann trên ROS và Gazebo." Can Tho University Journal of Science 59, ETMD (May 19, 2023): 63–69. http://dx.doi.org/10.22144/ctu.jvn.2023.030.

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Bài báo này được thực hiện nhằm nghiên cứu xe tự hành lái theo nguyên lý Ackermann. Mô hình xe được xây dựng để mô phỏng hệ thống định vị, tìm đường, bám đường dựa trên Gazebo và Robot Operating System (ROS). Mô phỏng thực hiện dựa trên cấu trúc Cây hành vi và các node được thiết lập trong ROS 2 nhằm đưa xe đến đúng vị trí theo yêu cầu. Kết quả mô phỏng cho thấy giải pháp đề xuất đã đáp ứng tốt hơn so với nguyên lý lái hai bánh chủ động và lái đa hướng khi thực hiện trên địa hình dễ bị trượt và gồ ghề. Kết quả khảo sát cho thấy robot di chuyển đến mục tiêu với độ lệch khoảng cách lớn hơn hoặc bằng 1 m đạt 96% và nhỏ hơn 1 m đạt 79%. Đồng thời, nghiên cứu này có thể làm tiền đề để triển khai mô hình thực tế trên cơ sở định vị bằng hệ thống GPS.
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42

Ha, Vo Thanh, and Vo Quang Vinh. "Experimental Research on Avoidance Obstacle Control for Mobile Robots Using Q-Learning (QL) and Deep Q-Learning (DQL) Algorithms in Dynamic Environments." Actuators 13, no. 1 (January 9, 2024): 26. http://dx.doi.org/10.3390/act13010026.

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This study provides simulation and experimental results on techniques for avoiding static and dynamic obstacles using a deep Q-learning (DQL) reinforcement learning algorithm for a two-wheel mobile robot with independent control. This method integrates the Q-learning (QL) algorithm with a neural network, where the neural networks in the DQL algorithm act as approximators for the Q matrix table for each pair (state–action). The effectiveness of the proposed solution was confirmed through simulations, programming, and practical experimentation. A comparison was drawn between the DQL algorithm and the QL algorithm. Initially, the mobile robot was connected to the control script using the Robot Operating System (ROS). The mobile robot was programmed in Python within the ROS operating system, and the DQL controller was programmed in Gazebo software. The mobile robot underwent testing in a workshop with various experimental scenarios considered. The DQL controller displayed improvements in computation time, convergence time, trajectory planning accuracy, and obstacle avoidance. As a result, the DQL controller surpassed the QL algorithm in terms of performance.
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43

Șomîtcă, Ioana-Alexandra, Stelian Brad, Vlad Florian, and Ștefan-Eduard Deaconu. "Improving Path Accuracy of Mobile Robots in Uncertain Environments by Adapted Bézier Curves." Electronics 11, no. 21 (November 1, 2022): 3568. http://dx.doi.org/10.3390/electronics11213568.

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An algorithm that presents the best possible approximation for the theoretical Bézier curve and the real path on which a mobile robot moves in a dynamic environment with mobile obstacles and boundaries is introduced in this paper. The algorithm is tested on a set of scenarios that comprehensively cover critical situations of obstacle avoidance. The selection of scenarios is made by deploying robot navigation performances into constraints and further into descriptive characteristics of the scenarios. Computer-simulated environments are created with dedicated tools (i.e., Gazebo) and modeling and programming technologies (i.e., Robot Operating System (ROS) and Python). It is shown that the proposed algorithm improves the performance of the path for robot navigation in a highly dynamic environment, with dense mobile obstacles.
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44

Shaju, Shawn, Thomas George, Jithin Kunnath Francis, Manu Joseph, and Mervin Joe Thomas. "Conceptual design and simulation study of an autonomous indoor medical waste collection robot." IAES International Journal of Robotics and Automation (IJRA) 12, no. 1 (March 1, 2023): 29. http://dx.doi.org/10.11591/ijra.v12i1.pp29-40.

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Solid waste management is one of the critical challenges seen everywhere, and the coronavirus disease (COVID-19) pandemic has only worsened the problems in the safe disposal of infectious waste. This paper outlines a design for a mobile robot that will intelligently identify, grasp, and collect a group of medical waste items using a six-degree of freedom (DoF) arm, You Only Look Once (YOLO) neural network, and a grasping algorithm. Various designs are generated before running simulations on the selected virtual model using Robot Operating System (ROS) and Gazebo simulator. A lidar sensor is also used to map the robot's surroundings and navigate autonomously. The robot has good scope for waste collection in medical facilities, where it can help create a safer environment.
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45

Zhao, Jianwei, Shengyi Liu, and Jinyu Li. "Research and Implementation of Autonomous Navigation for Mobile Robots Based on SLAM Algorithm under ROS." Sensors 22, no. 11 (May 31, 2022): 4172. http://dx.doi.org/10.3390/s22114172.

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Aiming at the problems of low mapping accuracy, slow path planning efficiency, and high radar frequency requirements in the process of mobile robot mapping and navigation in an indoor environment, this paper proposes a four-wheel drive adaptive robot positioning and navigation system based on ROS. By comparing and analyzing the mapping effects of various 2D-SLAM algorithms (Gmapping, Karto SLAM, and Hector SLAM), the Karto SLAM algorithm is used for map building. By comparing the Dijkstra algorithm with the A* algorithm, the A* algorithm is used for heuristic searches, which improves the efficiency of path planning. The DWA algorithm is used for local path planning, and real-time path planning is carried out by combining sensor data, which have a good obstacle avoidance performance. The mathematical model of four-wheel adaptive robot sliding steering was established, and the URDF model of the mobile robot was established under a ROS system. The map environment was built in Gazebo, and the simulation experiment was carried out by integrating lidar and odometer data, so as to realize the functions of mobile robot scanning mapping and autonomous obstacle avoidance navigation. The communication between the ROS system and STM32 is realized, the packaging of the ROS chassis node is completed, and the ROS chassis node has the function of receiving speed commands and feeding back odometer data and TF transformation, and the slip rate of the four-wheel robot in situ steering is successfully measured, making the chassis pose more accurate. Simulation tests and experimental verification show that the system has a high precision in environment map building and can achieve accurate navigation tasks.
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46

Molina-Leal, Alejandra, Alfonso Gómez-Espinosa, Jesús Arturo Escobedo Cabello, Enrique Cuan-Urquizo, and Sergio R. Cruz-Ramírez. "Trajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network." Applied Sciences 11, no. 22 (November 12, 2021): 10689. http://dx.doi.org/10.3390/app112210689.

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Autonomous mobile robots are an important focus of current research due to the advantages they bring to the industry, such as performing dangerous tasks with greater precision than humans. An autonomous mobile robot must be able to generate a collision-free trajectory while avoiding static and dynamic obstacles from the specified start location to the target location. Machine learning, a sub-field of artificial intelligence, is applied to create a Long Short-Term Memory (LSTM) neural network that is implemented and executed to allow a mobile robot to find the trajectory between two points and navigate while avoiding a dynamic obstacle. The input of the network is the distance between the mobile robot and the obstacles thrown by the LiDAR sensor, the desired target location, and the mobile robot’s location with respect to the odometry reference frame. Using the model to learn the mapping between input and output in the sample data, the linear and angular velocity of the mobile robot are obtained. The mobile robot and its dynamic environment are simulated in Gazebo, which is an open-source 3D robotics simulator. Gazebo can be synchronized with ROS (Robot Operating System). The computational experiments show that the network model can plan a safe navigation path in a dynamic environment. The best test accuracy obtained was 99.24%, where the model can generalize other trajectories for which it was not specifically trained within a 15 cm radius of a trained destination position.
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47

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 (June 21, 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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48

Lizzio, Fausto Francesco, Martin Bugaj, Ján Rostáš, and Stefano Primatesta. "Comparison of Multiple Models in Decentralized Target Estimation by a UAV Swarm." Drones 8, no. 1 (December 27, 2023): 5. http://dx.doi.org/10.3390/drones8010005.

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The decentralized estimation and tracking of a mobile target performed by a group of unmanned aerial vehicles (UAVs) is studied in this work. A flocking protocol is used for maintaining a collision-free formation, while a decentralized extended Kalman filter in the information form is employed to provide an estimate of the target state. In the prediction step of the filter, we adopt and compare three different models for the target motion with increasing levels of complexity, namely, a constant velocity (CV), a constant turn (CT), and a full-state (FS) model. Software-in-the-loop (SITL) simulations are conducted in ROS/Gazebo to compare the performance of the three models. The coupling between the formation and estimation tasks is evaluated since the tracking task is affected by the outcome of the estimation process.
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Guyonneau, Rémy, Franck Mercier, and Gabriel Freitas Oliveira Freitas. "LiDAR-Only Crop Navigation for Symmetrical Robot." Sensors 22, no. 22 (November 18, 2022): 8918. http://dx.doi.org/10.3390/s22228918.

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This paper presents a navigation approach for autonomous agricultural robots based on LiDAR data. This navigation approach is divided into two parts: a line finding algorithm and a control algorithm. The paper proposes several line finding algorithms (based on PEARL/Ruby approach) that extract lines from a LiDAR data set. Once the lines have been processed from the data set, a control algorithm filters these lines and, using a fuzzy controller, generates the wheel speed commands to move the robot among the crop rows. This navigation approach was tested using a simulator built on ROS middle-ware and Gazebo (the source codes of the simulation are available on GitHub). The results of the simulated experiments show that the proposed approach performs well for a large range of crop configurations (with or without considering weeds, with or without holes in the crop rows…).
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50

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 (January 19, 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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