An architecture for multi-robot localization and mapping in the Gazebo/Robot Operating System simulation environment

SIMULATION ◽  
2017 ◽  
Vol 93 (9) ◽  
pp. 771-780 ◽  
Author(s):  
Erkan Uslu ◽  
Furkan Çakmak ◽  
Nihal Altuntaş ◽  
Salih Marangoz ◽  
Mehmet Fatih Amasyalı ◽  
...  
Keyword(s):  
Operating System ◽  
Three Dimensional ◽  
Simulation Environment ◽  
Urban Search And Rescue ◽  
Mapping Algorithms ◽  
Robot Operating System ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Dimensional Simulation ◽  
Multi Robot

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.

scholarly journals Autonomous navigation and mapping in a simulated environment

2021 ◽  
Author(s):  
Benjamin Christie ◽  
Osama Ennasr ◽  
Garry Glaspell
Keyword(s):  
Operating System ◽  
Autonomous Navigation ◽  
Three Dimensional ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Robot Operating System ◽  
Simulated Environment ◽  
Localization And Mapping ◽  
Environment Exploration ◽  
Rgbd Cameras

Unknown Environment Exploration (UEE) with an Unmanned Ground Vehicle (UGV) is extremely challenging. This report investigates a frontier exploration approach, in simulation, that leverages Simultaneous Localization And Mapping (SLAM) to efficiently explore unknown areas by finding navigable routes. The solution utilizes a diverse sensor payload that includes wheel encoders, three-dimensional (3-D) LIDAR, and Red, Green, Blue and Depth (RGBD) cameras. The main goal of this effort is to leverage frontier-based exploration with a UGV to produce a 3-D map (up to 10 cm resolution). The solution provided leverages the Robot Operating System (ROS).

F1_Tenth_Course_Report_Group_C

2018 ◽  
Author(s):  
Yi Chen ◽  
Sagar Manglani ◽  
Roberto Merco ◽  
Drew Bolduc
Keyword(s):  
Operating System ◽  
Autonomous Driving ◽  
Software Tools ◽  
Simulation Environment ◽  
Robot Operating System ◽  
Available Information

In this paper, we discuss several of major robot/vehicle platforms available and demonstrate the implementation of autonomous techniques on one such platform, the F1/10. Robot Operating System was chosen for its existing collection of software tools, libraries, and simulation environment. We build on the available information for the F1/10 vehicle and illustrate key tools that will help achieve properly functioning hardware. We provide methods to build algorithms and give examples of deploying these algorithms to complete autonomous driving tasks and build 2D maps using SLAM. Finally, we discuss the results of our findings and how they can be improved.

scholarly journals A novel mobile robot navigation method based on deep reinforcement learning

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092167
Author(s):  
Hao Quan ◽  
Yansheng Li ◽  
Yi Zhang
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Mobile Robots ◽  
Recurrent Neural Network ◽  
Three Dimensional ◽  
Mobile Robot Navigation ◽  
Simulation Environment ◽  
Network Modules ◽  
Good Improvement ◽  
Dimensional Simulation

At present, the application of mobile robots is more and more extensive, and the movement of mobile robots cannot be separated from effective navigation, especially path exploration. Aiming at navigation problems, this article proposes a method based on deep reinforcement learning and recurrent neural network, which combines double net and recurrent neural network modules with reinforcement learning ideas. At the same time, this article designed the corresponding parameter function to improve the performance of the model. In order to test the effectiveness of this method, based on the grid map model, this paper trains in a two-dimensional simulation environment, a three-dimensional TurtleBot simulation environment, and a physical robot environment, and obtains relevant data for peer-to-peer analysis. The experimental results show that the proposed algorithm has a good improvement in path finding efficiency and path length.

scholarly journals Gerak Robot Berkaki Dua menggunakan ROS dan RViz sebagai Visualisasi Interaktif

2021 ◽  
Vol 9 (1) ◽  
pp. 43
Author(s):  
ADI SUCIPTO ◽  
RADEN SANGGAR DEWANTO ◽  
DADET PRAMADIHANTO
Keyword(s):  
Operating System ◽  
Success Rate ◽  
Operating Systems ◽  
Three Dimensional ◽  
Biped Robot ◽  
Sensor Data ◽  
Average Error ◽  
Robot Operating System ◽  
Robot Technology ◽  
The Right

ABSTRAKPengembangan sistem operasi pada bidang robotika telah menjadi fokus utama pada era ini. Salah satu perkembangan sistem operasi pada teknologi robot saat ini adalah Robot Operating System (ROS) dengan RViz. ROS merupakan sistem operasi berbasis library dan beberapa tools untuk mengembangkan suatu program pada robot, sedangkan RViz merupakan visualisasi tiga dimensi yang dapat digunakan untuk memvisualisasikan robot dan data sensor dynamixel. Pada Penelitian kali ini, peneliti membuat simulasi beberapa gerakan yang dilakukan pada RViz dan kemudian diimplementasikan pada robot. Tingkat keberhasilan dari perencanaan gerakan ini memiliki rata rata error sebesar 1.8%. Gerakan condong ke kiri memiliki rata-rata error sebesar 0.83%. Gerakan condong ke kanan memiliki rata-rata error sebesar 0.84%. Gerakan mengangkat satu kaki memiliki rata-rata error sebesar 1.71%. Gerakan kaki kanan ke depan memiliki rata-rata error sebesar 3.83%.Kata kunci: Robot Berkaki Dua, Robot Operating System (ROS), RViz (rosvisualization), Dynamixel Controller, Data Sensor Dynamixel. ABSTRACTThe development of operating systems in the field of robotics has become the main focus of this era. One of the operating system developments in robot technology today is the Robot Operating System (ROS) with RViz. ROS is a library-based operating system and several tools for developing a program on robots, while RVIZ is a three-dimensional visualization that can be used to visualize robots and dynamixel sensor data. In this study, researchers made a simulation of some of the movements carried out on RViz and then implemented on robots. The success rate of planning this movement has an average error of 1.8%. Leaning to the left has an average error of 0.83%. Leaning to the right has an average error of 0.84%. One leg lift has an average error of 1.71%. The movement of the right foot forward has an average error of 3.83%.Keywords: Biped Robot, Robot Operating System (ROS), RViz (Ros-Visualization), Dynamixel Controller, Sensor Dynamixel Data.

Mathematical modeling of unmanned movement aircraft - four-rotor

2020 ◽  
Vol 2 (3) ◽  
pp. 149-163
Author(s):  
Jerzy Kisilowski ◽  
Rafał Kowalik ◽  
Łukasz Parszutowicz
Keyword(s):  
Control System ◽  
Three Dimensional ◽  
Programming Environment ◽  
Simulation Environment ◽  
Rectangular Coordinate System ◽  
Model Study ◽  
Model Control ◽  
Data Source ◽  
Dimensional Simulation ◽  
Rotor Model

The article presents an analytical approach to building a mathematical model of a quadrocopter. The main purpose of building the model was to design an appropriate facility control system and analyze its behavior in various situations. The assumption was made to build a model, control system and all accompanying algorithms in an open programming environment, which will allow their subsequent implementation in a real facility, without the need to use expensive software. The quadrocopter is controlled by the operator by means of hand movements that are read by the camera and properly interpreted using advanced image processing methods. The entire system is visualized and embedded in a three-dimensional simulation environment. The model study was conducted using a DC motor as an input data source. The operation of the model was checked with a controller when a disturbance was introduced into the model. The four-rotor model with a selected regulator was tested by analyzing the angular velocity and position of the object in a rectangular coordinate system. At the end of the article, the results of the simulations made are presented and the resulting conclusions are presented.

scholarly journals Implementation and Reconfiguration of Robot Operating System on Human Follower Transporter Robot

2015 ◽  
Vol 9 (2) ◽  
pp. 59
Author(s):  
Addythia Saphala ◽  
Prianggada Indra Tanaya
Keyword(s):  
Operating System ◽  
Mobile Robot ◽  
Navigation System ◽  
Robot Navigation ◽  
Simultaneous Localization And Mapping ◽  
Kinect Sensor ◽  
Operation System ◽  
Robot Operating System ◽  
Localization And Mapping

Robotic Operation System (ROS) is an im- portant platform to develop robot applications. One area of applications is for development of a Human Follower Transporter Robot (HFTR), which  can  be  considered  as a custom mobile robot utilizing differential driver steering method and equipped with Kinect sensor. This study discusses the development of the robot navigation system by implementing Simultaneous Localization and Mapping (SLAM).

scholarly journals Cooperative simultaneous localization and mapping algorithm based on distributed particle filter

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141881995 ◽  
Author(s):  
Shuhuan Wen ◽  
Jian Chen ◽  
Xiaohan Lv ◽  
Yongzheng Tong
Keyword(s):  
Particle Filter ◽  
Posterior Probability ◽  
Simultaneous Localization And Mapping ◽  
Estimation Algorithm ◽  
Mapping Algorithm ◽  
Global Estimation ◽  
Localization And Mapping ◽  
Mapping System ◽  
Distributed Particle Filter ◽  
Multi Robot

In this article, cooperative simultaneous localization and mapping algorithm based on distributed particle filter is proposed for multi-robot cooperative simultaneous localization and mapping system. First, a multi-robot cooperative simultaneous localization and mapping system model is established based on Rao-Blackwellised particle filter and simultaneous localization and mapping (FastSLAM 2.0) algorithm, and an median of the local posterior probability (MP)-cooperative simultaneous localization and mapping algorithm combined with the M-posterior distributed estimation algorithm is proposed. Then, according to the accuracy advantage of the early landmarks comparing to the later landmarks in the simultaneous localization and mapping task, an improved time-median of the local posterior probability (MP)-cooperative simultaneous localization and mapping algorithm based on time difference optimization is proposed, which optimizes the weights of the local estimation and improves the accuracy of the global estimation. The simulation results show that the algorithm is practical and effective.

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