scholarly journals Hardware in the Loop Topology for an Omnidirectional Mobile Robot Using Matlab in a Robot Operating System Environment

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 969
Author(s):  
Constantin-Catalin Dosoftei ◽  
Alexandru-Tudor Popovici ◽  
Petru-Razvan Sacaleanu ◽  
Paul-Marcelin Gherghel ◽  
Cristina Budaciu
Keyword(s):  
Operating System ◽  
Path Planning ◽  
Mobile Robot ◽  
Autonomous Navigation ◽  
Vertical Axis ◽  
Hardware In The Loop ◽  
Robot Operating System ◽  
System Nodes ◽  
Navigation Software ◽  
Time Autonomous

The symmetry of the omnidirectional robot motion abilities around its central vertical axis is an important advantage regarding its driveability for the flexible interoperation with fixed conveyor systems. The paper illustrates a Hardware in the Loop architectural approach for integrated development of an Ominidirectional Mobile Robot that is designed to serve in a dynamic logistic environment. Such logistic environments require complex algorithms for autonomous navigation between different warehouse locations, that can be efficiently developed using Robot Operating System nodes. Implementing path planning nodes benefits from using Matlab-Simulink, which provides a large selection of algorithms that are easily integrated and customized. The proposed solution is deployed for validation on a NVIDIA Jetson Nano, the embedded computer hosted locally on the robot, that runs the autonomous navigation software. The proposed solution permits the live connection to the omnidirectional prototype platform, allowing to deploy algorithms and acquire data for debugging the location, path planning and the mapping information during real time autonomous navigation experiments, very useful in validating different strategies.

CPC Algorithm: Exact Area Coverage by a Mobile Robot Using Approximate Cellular Decomposition

Robotica ◽  
2020 ◽  
pp. 1-22
Author(s):  
K. R. Guruprasad ◽  
T. D. Ranjitha
Keyword(s):  
Operating System ◽  
Path Planning ◽  
Mobile Robot ◽  
Formal Analysis ◽  
Area Coverage ◽  
Cell Permeability ◽  
Cellular Decomposition ◽  
Robot Operating System ◽  
Coverage Path Planning

SUMMARY A new coverage path planning (CPP) algorithm, namely cell permeability-based coverage (CPC) algorithm, is proposed in this paper. Unlike the most CPP algorithms using approximate cellular decomposition, the proposed algorithm achieves exact coverage with lower coverage overlap compared to that with the existing algorithms. Apart from a formal analysis of the algorithm, the performance of the proposed algorithm is compared with two representative approximate cellular decomposition-based coverage algorithms reported in the literature. Results of demonstrative experiments on a TurtleBot mobile robot within the robot operating system/Gazebo environment and on a Fire Bird V robot are also provided.

scholarly journals ROS based Autonomous Robot with Open CV

2019 ◽  
Vol 9 (1S) ◽  
pp. 249-251
Keyword(s):  
Operating System ◽  
Mobile Robot ◽  
Autonomous Navigation ◽  
General Purpose ◽  
Autonomous Robot ◽  
Robot Operating System ◽  
Image Tracking

In this project, we have designed and developed an autonomous robot that is powered by Robot Operating System (ROS). The capabilities of the robot include autonomous navigation, image tracking and mapping. OpenCV has been implemented in the on-board microprocessor to process the images that are captured by the general purpose webcams on the robot. A microcontroller has also been used to control the motors. The ultimate aim of this project is to develop a mobile robot capable of making its own decisions based on the images received.

scholarly journals Mobile Robot Simulation and Navigation in ROS and Gazebo

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Operating System ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Autonomous Navigation ◽  
The Other ◽  
Robot Simulation ◽  
Daily Lives ◽  
Robot Operating System ◽  
Future Production ◽  
Navigation Mode

mobile robots are entering our daily lives as wellas in the industry. Their task is usually associated with carryingout transportation. This leads to the need to performautonomous movement of mobile robots. On the other hand,modern practice is that the planning of most processes is donethrough simulations. Thus, various future production problemscan be anticipated and remedied or improved. The articledescribes the creation of a mobile robot model in the Gazebosimulation environment. Specific settings and features forrunning a mobile robot in autonomous navigation mode underthe robot operating system are presented. The steps for creatinga map, localization and navigation are presented. Experimentshave been conducted to optimize and tune the parameters ofboth the robot model itself and the simulation controlparameters.

A Study of Autonomous Navigation of a Robot Model based on SLAM, ROS, and Kinect

2018 ◽  
Vol 7 (3.33) ◽  
pp. 28
Author(s):  
Asilbek Ganiev ◽  
Kang Hee Lee
Keyword(s):  
Operating System ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Autonomous Navigation ◽  
Simultaneous Localization And Mapping ◽  
Target Point ◽  
Robot Operating System ◽  
Model Based ◽  
Localization And Mapping ◽  
Starting Point

In this paper, we used a robot operating system (ROS) that is designed to work with mobile robots. ROS provides us with simultaneous localization and mapping of the environment, and here it is used to autonomously navigate a mobile robot simulator between specified points. Also, when the mobile robot automatically navigates between the starting point and the target point, it bypasses obstacles; and if necessary, sets a new path of the route to reach the goal point.  

scholarly journals Autonomous Path Planning Scheme Research for Mobile Robot

2016 ◽  
Vol 16 (4) ◽  
pp. 113-125
Author(s):  
Jianxian Cai ◽  
Xiaogang Ruan ◽  
Pengxuan Li
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Autonomous Navigation ◽  
Learning Algorithm ◽  
Action Learning ◽  
Cognitive Learning ◽  
Learning Ability ◽  
Q Learning ◽  
Planning Strategy ◽  
Navigation Path

Abstract An autonomous path-planning strategy based on Skinner operant conditioning principle and reinforcement learning principle is developed in this paper. The core strategies are the use of tendency cell and cognitive learning cell, which simulate bionic orientation and asymptotic learning ability. Cognitive learning cell is designed on the base of Boltzmann machine and improved Q-Learning algorithm, which executes operant action learning function to approximate the operative part of robot system. The tendency cell adjusts network weights by the use of information entropy to evaluate the function of operate action. The results of the simulation experiment in mobile robot showed that the designed autonomous path-planning strategy lets the robot realize autonomous navigation path planning. The robot learns to select autonomously according to the bionic orientate action and have fast convergence rate and higher adaptability.

Navigation of a wheeled mobile robotic agent using modified grey wolf optimization controller

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chittaranjan Paital ◽  
Saroj Kumar ◽  
Manoj Kumar Muni ◽  
Dayal R. Parhi ◽  
Prasant Ranjan Dhal
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Real Time ◽  
Autonomous Navigation ◽  
Simulation Software ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Content Type ◽  
Cluttered Environment

PurposeSmooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile robot. These are important aspects of the mobile robot during autonomous navigation in any workspace. Navigation of mobile robots includes reaching the target from the start point by avoiding obstacles in a static or dynamic environment. Several techniques have already been proposed by the researchers concerning navigational problems of the mobile robot still no one confirms the navigating path is optimal.Design/methodology/approachTherefore, the modified grey wolf optimization (GWO) controller is designed for autonomous navigation, which is one of the intelligent techniques for autonomous navigation of wheeled mobile robot (WMR). GWO is a nature-inspired algorithm, which mainly mimics the social hierarchy and hunting behavior of wolf in nature. It is modified to define the optimal positions and better control over the robot. The motion from the source to target in the highly cluttered environment by negotiating obstacles. The controller is authenticated by the approach of V-REP simulation software platform coupled with real-time experiment in the laboratory by using Khepera-III robot.FindingsDuring experiments, it is observed that the proposed technique is much efficient in motion control and path planning as the robot reaches its target position without any collision during its movement. Further the simulation through V-REP and real-time experimental results are recorded and compared against each corresponding results, and it can be seen that the results have good agreement as the deviation in the results is approximately 5% which is an acceptable range of deviation in motion planning. Both the results such as path length and time taken to reach the target is recorded and shown in respective tables.Originality/valueAfter literature survey, it may be said that most of the approach is implemented on either mathematical convergence or in mobile robot, but real-time experimental authentication is not obtained. With a lack of clear evidence regarding use of MGWO (modified grey wolf optimization) controller for navigation of mobile robots in both the environment, such as in simulation platform and real-time experimental platforms, this work would serve as a guiding link for use of similar approaches in other forms of robots.

Improved Wall-following Driving for Robot Operating System-based High-speed Autonomous Mobile Robot

2019 ◽  
Vol 28 (3) ◽  
pp. 156-165
Author(s):  
Seunghan Han ◽  
Yongrae Choi ◽  
Jaepil Yang ◽  
Hyungjun Hwang ◽  
Kihun Kim ◽  
...  
Keyword(s):  
Operating System ◽  
Mobile Robot ◽  
High Speed ◽  
Autonomous Mobile Robot ◽  
Robot Operating System ◽  
Wall Following
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