Methods of Mobile Robot Visual Navigation and Environment Mapping

Pershina, J. S.; Kazdorf, S.Ya.; Lopota, A. V.

doi:10.3103/s8756699019020109

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…Te navigation of robotics denotes the activity of a robot to control its location in relation to a specifc reference and then outline a track to arrive at the wanted location. It can contain a set of jobs such as track planning, localization, and mapping [19]. Te procedure for building a map demands large amounts of storage space and is computationally heavy.…”

Section: Research Methods and Toolsmentioning

confidence: 99%

Real-Time SLAM Mobile Robot and Navigation Based on Cloud-Based Implementation

Abdulsaheb

Kadhim

2023

Journal of Robotics

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This study investigates the feasibility of a mobile robot navigating and discovering its location in unknown environments, followed by the creation of maps of these navigated environments for future use. First, a real mobile robot named TurtleBot3 Burger was used to achieve the simultaneous localization and mapping (SLAM) technique for a complex environment with 12 obstacles of different sizes based on the Rviz library, which is built on the robot operating system (ROS) booted in Linux. It is possible to control the robot and perform this process remotely by using an Amazon Elastic Compute Cloud (Amazon EC2) instance service. Then, the map to the Amazon Simple Storage Service (Amazon S3) cloud was uploaded. This provides a database to display maps and use them at any time for navigation without the need to redraw the map. This map can be accessed by using an authentication process (username and password) supervised by the cloud server administrator. After that, using the serverless image handler (SIH), with the aid of this solution, you can change the size of images, change the color of the background, format them, or add watermarks. Experiment results demonstrated the ability to build a map of an unknown location in a complex environment and use it for navigation tasks on a real mobile robot via remote control. It also showed the success of the process of storing the map for future use and the process of modifying the map using SIH.

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Section: Research Methods and Toolsmentioning

confidence: 99%

Real-Time SLAM Mobile Robot and Navigation Based on Cloud-Based Implementation

Abdulsaheb

Kadhim

2023

Journal of Robotics

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“…The latest research trends in robotics have witnessed dramatic advancements in using robots to facilitate human difficult jobs such as the survey of mars or analyses of the sea bottom such applications need that the robot can navigate in unknown environments [1][2][3][4][5][6][7]. The navigation issue is considered one of the most important issues in current research trends in robotics [8][9][10][11]. A key condition for successful navigation is the capability of localization and building maps of unknown environments for a mobile robot, which is also well-known as simultaneous localization and mapping (SLAM) [12,13].…”

Section: Introductionmentioning

confidence: 99%

Developing a real time navigation for the mobile robots at unknown environments

Abdulredah

Kadhim

2020

IJEECS

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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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“…Therefore, many experts and scholars have performed relevant studies on navigation positioning technology in greenhouse robots [4,5]. The navigation positioning technology presently used by greenhouse robots primarily includes interior conventional navigation positioning technology [6][7][8][9] and simultaneous localization and mapping (SLAM) [10,11]. The basic principles of the conventional navigation positioning technology of interior robots is that a robot that moves within a given environment employs Wi-Fi [6,7], inertial navigation [8] and sensing systems such as visual navigation [9] to cover the room, obtain information on the operational environment and set the navigation parameters of the robot through calculation and motion planning, and then track the preset navigation path of the robot and perform realtime corrections.…”

Section: Introductionmentioning

confidence: 99%

“…The navigation positioning technology presently used by greenhouse robots primarily includes interior conventional navigation positioning technology [6][7][8][9] and simultaneous localization and mapping (SLAM) [10,11]. The basic principles of the conventional navigation positioning technology of interior robots is that a robot that moves within a given environment employs Wi-Fi [6,7], inertial navigation [8] and sensing systems such as visual navigation [9] to cover the room, obtain information on the operational environment and set the navigation parameters of the robot through calculation and motion planning, and then track the preset navigation path of the robot and perform realtime corrections. For example, for the interior positioning system of a robot based on Wi-Fi, it is necessary for the robot to obtain more than 3 wireless sensing signals periodically, and according to the strength of the signal, to analyze its own position after the calculation, which involves a relatively large calculation and test workload [12].…”

Section: Introductionmentioning

confidence: 99%

Integrated Navigation by a Greenhouse Robot Based on an Odometer/Lidar

Shi¹,

Wang²,

Tian³

et al. 2020

I2M

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During greenhouse operations, robots need a specific working path to perform highprecision cruises, and thus, we designed a navigation positioning system based on an odometer/lidar. The navigation positioning system consists of a supervision terminal and a mobile robot. The supervision terminal releases map composition and cruise tasks, and the mobile robot composes a two-dimensional environment map, plans the cruise path and engages in navigation positioning; together, the two perform remote data exchanges through a wireless network. The robot could collect encoder data and obtain mileage information through track deduction, and combined with lidar data and the Gmapping algorithm, a two-dimensional environmental map was established. This system employs an A* algorithm to plan the cruise path and uses AMCL to estimate the position and pose of the robot. Based on the application of an expandable A* algorithm in the navigation toolkit of ROS, a specific working path cruise could be performed by setting goal points. The test results show that the navigation positioning system could perform a specific working path cruise; the average deviation in its straight line walk is 3.34 cm. The average deviation in the specific working path cruise is 2.73 cm, and the system has relatively higher navigation positioning precision because it could finish the specific path cruise and could better satisfy the greenhouse navigation positioning requirements than previous options.

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Methods of Mobile Robot Visual Navigation and Environment Mapping

Cited by 7 publications

References 30 publications

Real-Time SLAM Mobile Robot and Navigation Based on Cloud-Based Implementation

Real-Time SLAM Mobile Robot and Navigation Based on Cloud-Based Implementation

Developing a real time navigation for the mobile robots at unknown environments

Integrated Navigation by a Greenhouse Robot Based on an Odometer/Lidar

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