Robot visual navigation estimation and target localization based on neural network
The high computational cost, complex external environment, and limited computing resources of embedded system are some major problems in traditional autonomous robot navigation methods. To overcome these problems, a mobile robot path planning navigation system based on panoramic vision was proposed....
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2022-08-01
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| Series: | Paladyn |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/pjbr-2022-0005 |
| _version_ | 1797669088706691072 |
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| author | Zhao Yanping Gupta Rajeev Kumar Onyema Edeh Michael |
| author_facet | Zhao Yanping Gupta Rajeev Kumar Onyema Edeh Michael |
| author_sort | Zhao Yanping |
| collection | DOAJ |
| description | The high computational cost, complex external environment, and limited computing resources of embedded system are some major problems in traditional autonomous robot navigation methods. To overcome these problems, a mobile robot path planning navigation system based on panoramic vision was proposed. This method first describes the structure and functions of the navigation system. It explains how to use the environment to explore and map in order to create a panoramic vision sensor. Finally, it elaborates on the breadth-first search based on regression neural network (RNN) method, the Voronoi skeleton diagram method, the algorithm principle, and how to navigate by the planning path implementation of practical strategies. The simulation results illustrate that the breadth-first search method and the Voronoi skeleton graph method based on panoramic view have a high speed. The accessibility of RNN planning algorithm can effectively solve the difficult problems such as high computing overhead, complex navigation environment, and limited computing resources. In the actual robot navigation experiment, the difference in real-time performance and optimality performance that exists between the two algorithms is reflected in the length and duration of the course taken by the robot. When applied to a variety of site environments, the breadth-first search method requires between 23.2 and 45.3% more time to calculate the planned path than the Voronoi skeleton graph method, despite the fact that the planned path length is between 20.7 and 35.9% shorter using the breadth-first search method. It serves as a guide for choosing the appropriate algorithm to implement in practical applications. |
| first_indexed | 2024-03-11T20:38:24Z |
| format | Article |
| id | doaj.art-76ff39fdaf754f859966b5c7924108df |
| institution | Directory Open Access Journal |
| issn | 2081-4836 |
| language | English |
| last_indexed | 2024-03-11T20:38:24Z |
| publishDate | 2022-08-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Paladyn |
| spelling | doaj.art-76ff39fdaf754f859966b5c7924108df2023-10-02T04:13:54ZengDe GruyterPaladyn2081-48362022-08-01131768310.1515/pjbr-2022-0005Robot visual navigation estimation and target localization based on neural networkZhao Yanping0Gupta Rajeev Kumar1Onyema Edeh Michael2School of Electronic Information Engineering, Anhui Technical College of Water Resources and Hydroelectric Power, Hefei, Anhui, 231603, ChinaDepartment of Computer Science & Engineering, Pandit Deendayal Energy University, Gandhinagar, 382007, IndiaDepartment of Mathematics and Computer Science, Coal City University, Enugu, 400107, NigeriaThe high computational cost, complex external environment, and limited computing resources of embedded system are some major problems in traditional autonomous robot navigation methods. To overcome these problems, a mobile robot path planning navigation system based on panoramic vision was proposed. This method first describes the structure and functions of the navigation system. It explains how to use the environment to explore and map in order to create a panoramic vision sensor. Finally, it elaborates on the breadth-first search based on regression neural network (RNN) method, the Voronoi skeleton diagram method, the algorithm principle, and how to navigate by the planning path implementation of practical strategies. The simulation results illustrate that the breadth-first search method and the Voronoi skeleton graph method based on panoramic view have a high speed. The accessibility of RNN planning algorithm can effectively solve the difficult problems such as high computing overhead, complex navigation environment, and limited computing resources. In the actual robot navigation experiment, the difference in real-time performance and optimality performance that exists between the two algorithms is reflected in the length and duration of the course taken by the robot. When applied to a variety of site environments, the breadth-first search method requires between 23.2 and 45.3% more time to calculate the planned path than the Voronoi skeleton graph method, despite the fact that the planned path length is between 20.7 and 35.9% shorter using the breadth-first search method. It serves as a guide for choosing the appropriate algorithm to implement in practical applications.https://doi.org/10.1515/pjbr-2022-0005panoramic visionregression neural networkpath planningnavigation |
| spellingShingle | Zhao Yanping Gupta Rajeev Kumar Onyema Edeh Michael Robot visual navigation estimation and target localization based on neural network Paladyn panoramic vision regression neural network path planning navigation |
| title | Robot visual navigation estimation and target localization based on neural network |
| title_full | Robot visual navigation estimation and target localization based on neural network |
| title_fullStr | Robot visual navigation estimation and target localization based on neural network |
| title_full_unstemmed | Robot visual navigation estimation and target localization based on neural network |
| title_short | Robot visual navigation estimation and target localization based on neural network |
| title_sort | robot visual navigation estimation and target localization based on neural network |
| topic | panoramic vision regression neural network path planning navigation |
| url | https://doi.org/10.1515/pjbr-2022-0005 |
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