Autonomously making a map, localizing within it, and planning with it are fundamental problems in mobile robotics. Every autonomous mobile robot system must include a solution to all three problems. These three problems are interconnected, with simultaneous localization and mapping (SLAM) being a well-known issue. However, there is indeed a growing and developing realization in the research field that path planning how a robot goes about mapping and finding an environment (and then operating in the environment such as starting to the destination point) can avoid degenerate conditions and greatly reduce SLAM complexity. In this paper, the implementation of an autonomous mobile robot system for indoor environments using open-source ROS packages and a combination of cartography algorithm and adaptive Monte Carlo localization (AMCL) algorithms has been implemented. The system addresses the challenge of developing three components such as mapping, localization, and path planning systems for indoor autonomous mobile robots. The mapping module creates a global map using the cartography ROS package and SLAM algorithm. The localization module estimates the robot's pose using the AMCL approach. The planning module generates collision-free trajectories and control commands using the moving base ROS package. The experimental results demonstrate the effectiveness of this approach and its valuable contribution to the robotics field. The cartography algorithm mapping algorithm generates accurate and reliable maps, while the localization algorithm successfully determines the robot's position with good performance. Additionally, the path planning algorithm effectively avoids both static and dynamic obstacles, ensuring smooth navigation in the environment.
| Published in | Frontiers (Volume 4, Issue 3) |
| DOI | 10.11648/j.frontiers.20240403.13 |
| Page(s) | 91-100 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Autonomous Mobile Robot, Mapping, Localization, Cartography, AMCL
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APA Style
Tola, T. A., Mi, J., Che, Y. Q. (2024). Mapping and Localization of Autonomous Mobile Robots in Simulated Indoor Environments. Frontiers, 4(3), 91-100. https://doi.org/10.11648/j.frontiers.20240403.13
ACS Style
Tola, T. A.; Mi, J.; Che, Y. Q. Mapping and Localization of Autonomous Mobile Robots in Simulated Indoor Environments. Frontiers. 2024, 4(3), 91-100. doi: 10.11648/j.frontiers.20240403.13
AMA Style
Tola TA, Mi J, Che YQ. Mapping and Localization of Autonomous Mobile Robots in Simulated Indoor Environments. Frontiers. 2024;4(3):91-100. doi: 10.11648/j.frontiers.20240403.13
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Download@article{10.11648/j.frontiers.20240403.13,
author = {Tsegaye Alemu Tola and Jing Mi and Yan qiu Che},
title = {Mapping and Localization of Autonomous Mobile Robots in Simulated Indoor Environments
},
journal = {Frontiers},
volume = {4},
number = {3},
pages = {91-100},
doi = {10.11648/j.frontiers.20240403.13},
url = {https://doi.org/10.11648/j.frontiers.20240403.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.frontiers.20240403.13},
abstract = {Autonomously making a map, localizing within it, and planning with it are fundamental problems in mobile robotics. Every autonomous mobile robot system must include a solution to all three problems. These three problems are interconnected, with simultaneous localization and mapping (SLAM) being a well-known issue. However, there is indeed a growing and developing realization in the research field that path planning how a robot goes about mapping and finding an environment (and then operating in the environment such as starting to the destination point) can avoid degenerate conditions and greatly reduce SLAM complexity. In this paper, the implementation of an autonomous mobile robot system for indoor environments using open-source ROS packages and a combination of cartography algorithm and adaptive Monte Carlo localization (AMCL) algorithms has been implemented. The system addresses the challenge of developing three components such as mapping, localization, and path planning systems for indoor autonomous mobile robots. The mapping module creates a global map using the cartography ROS package and SLAM algorithm. The localization module estimates the robot's pose using the AMCL approach. The planning module generates collision-free trajectories and control commands using the moving base ROS package. The experimental results demonstrate the effectiveness of this approach and its valuable contribution to the robotics field. The cartography algorithm mapping algorithm generates accurate and reliable maps, while the localization algorithm successfully determines the robot's position with good performance. Additionally, the path planning algorithm effectively avoids both static and dynamic obstacles, ensuring smooth navigation in the environment.
},
year = {2024}
}
TY - JOUR T1 - Mapping and Localization of Autonomous Mobile Robots in Simulated Indoor Environments AU - Tsegaye Alemu Tola AU - Jing Mi AU - Yan qiu Che Y1 - 2024/09/23 PY - 2024 N1 - https://doi.org/10.11648/j.frontiers.20240403.13 DO - 10.11648/j.frontiers.20240403.13 T2 - Frontiers JF - Frontiers JO - Frontiers SP - 91 EP - 100 PB - Science Publishing Group SN - 2994-7197 UR - https://doi.org/10.11648/j.frontiers.20240403.13 AB - Autonomously making a map, localizing within it, and planning with it are fundamental problems in mobile robotics. Every autonomous mobile robot system must include a solution to all three problems. These three problems are interconnected, with simultaneous localization and mapping (SLAM) being a well-known issue. However, there is indeed a growing and developing realization in the research field that path planning how a robot goes about mapping and finding an environment (and then operating in the environment such as starting to the destination point) can avoid degenerate conditions and greatly reduce SLAM complexity. In this paper, the implementation of an autonomous mobile robot system for indoor environments using open-source ROS packages and a combination of cartography algorithm and adaptive Monte Carlo localization (AMCL) algorithms has been implemented. The system addresses the challenge of developing three components such as mapping, localization, and path planning systems for indoor autonomous mobile robots. The mapping module creates a global map using the cartography ROS package and SLAM algorithm. The localization module estimates the robot's pose using the AMCL approach. The planning module generates collision-free trajectories and control commands using the moving base ROS package. The experimental results demonstrate the effectiveness of this approach and its valuable contribution to the robotics field. The cartography algorithm mapping algorithm generates accurate and reliable maps, while the localization algorithm successfully determines the robot's position with good performance. Additionally, the path planning algorithm effectively avoids both static and dynamic obstacles, ensuring smooth navigation in the environment. VL - 4 IS - 3 ER -
Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, P. R. China
School of Vocational Education, Tianjin University of Technology and Education, Tianjin, P. R. China
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