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      The Path Planning and Location Method of Inspection Robot in a Large Storage Tank Bottom

      research-article
      1 , 1 , , 2 , 2 ,
      Computational Intelligence and Neuroscience
      Hindawi

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          Abstract

          With the development of robot technology, inspection robots have been applied to the defect detection of large tanks. However, the existing path planning algorithm of the tank bottom detection robot is easy to fall into the local minimum, and the path is not smooth. Besides, the positioning of the tank bottom detection robot is not accurate. This article proposes a path planning and location algorithm for the large tank bottom detection robot. Specifically, we design a preset spiral path according to the shape of the tank bottom, and a rotating potential field (RPF) near the obstacle is added to avoid the problem of path planning falling into a local minimum. We obtained accurate and smooth planning results. Compared with the state-of-the-art, the RPF method reduced the average RMSE by 9.49%. In addition, by measuring the acoustic emission distance, the three-point positioning algorithm can be used to achieve the calculation of the robot position detection in the proposed method, and the average positioning error on the spiral path is only 0.0748 ± 0.0032.

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          Most cited references42

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          Real-Time Obstacle Avoidance for Manipulators and Mobile Robots

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            PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R

            Summary: Precision-recall (PR) and receiver operating characteristic (ROC) curves are valuable measures of classifier performance. Here, we present the R-package PRROC, which allows for computing and visualizing both PR and ROC curves. In contrast to available R-packages, PRROC allows for computing PR and ROC curves and areas under these curves for soft-labeled data using a continuous interpolation between the points of PR curves. In addition, PRROC provides a generic plot function for generating publication-quality graphics of PR and ROC curves. Availability and implementation: PRROC is available from CRAN and is licensed under GPL 3. Contact: grau@informatik.uni-halle.de
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              Mobile robot path planning using membrane evolutionary artificial potential field

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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2023
                2 March 2023
                : 2023
                : 3029545
                Affiliations
                1College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
                2China Special Equipment Inspection and Research Institute, Beijing 100026, China
                Author notes

                Academic Editor: Nouman Ali

                Author information
                https://orcid.org/0000-0001-6850-3817
                https://orcid.org/0000-0002-0609-3610
                https://orcid.org/0009-0008-3174-4252
                https://orcid.org/0009-0003-8046-7523
                Article
                10.1155/2023/3029545
                9998163
                36909973
                e9161ec9-1bb3-46bc-af34-59e71e4d7cec
                Copyright © 2023 Yinchu Wang et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 July 2022
                : 5 February 2023
                : 21 February 2023
                Funding
                Funded by: Research and Application of Tank Inspection Robot for Safety of National Petroleum Strategic Reserve
                Award ID: 2019YFB1310700
                Funded by: National Natural Science Foundation of China
                Award ID: 61672084
                Categories
                Research Article

                Neurosciences
                Neurosciences

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