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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JERA</journal-id><journal-title-group><journal-title>Journal of Electronic Research and Application</journal-title></journal-title-group><issn>2208-3502</issn><eissn>2208-3510</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/jera.v9i1.9460</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Path Planning for Thermal Power Plant Fan Inspection Robot Based on Improved A* Algorithm</title><url>https://artdesignp.com/journal/JERA/9/1/10.26689/jera.v9i1.9460</url><author>ZhangWei,ZhangTingfeng</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>9</volume><issue>1</issue><history><date date-type="pub"><published-time>2025-02-17</published-time></date></history><abstract>To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants, this paper proposes an intelligent inspection robot path planning scheme based on an improved A* algorithm. The inspection robot utilizes multiple sensors to monitor key parameters of the fans, such as vibration, noise, and bearing temperature, and upload the data to the monitoring center. The robot’s inspection path employs the improved A* algorithm, incorporating obstacle penalty terms, path reconstruction, and smoothing optimization techniques, thereby achieving optimal path planning for the inspection robot in complex environments. Simulation results demonstrate that the improved A* algorithm significantly outperforms the traditional A* algorithm in terms of total path distance, smoothness, and detour rate, effectively improving the execution efficiency of inspection tasks.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Zhou Y, Wang W, Li Z, et al., 2020, Application Research on Path Planning of Mobile Robots Based on A Algorithm. Computer Knowledge and Technology, 2020, 16(13): 1–3 + 10.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Delobel L, Aufrere R, Debain C, et al., 2019, A Real-Time Map Refinement Method Using a Multi-Sensor Localization Framework. IEEE Transactions on Intelligent Transportation Systems, 20(5): 1644–1658.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B3" content-type="article"><label>3</label><element-citation publication-type="journal"><p>Lin H, Dan X, Jian O, et al., 2021, Review of Path Planning Algorithms for Mobile Robots. Computer Engineering and Applications, 57(18): 38–48.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B4" content-type="article"><label>4</label><element-citation publication-type="journal"><p>Ouyang M, Ma Y, 2020, Path Planning for Gravity Aided Navigation Based on Improved A* Algorithm. Chinese</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B5" content-type="article"><label>5</label><element-citation publication-type="journal"><p>Journal of Geophysics, 63(12): 4361–4368.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B6" content-type="article"><label>6</label><element-citation publication-type="journal"><p>Lai R, Dou L, Wu Z, et al., 2024, Fusion of Improved A* and Dynamic Window Approach for Mobile Robot</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B7" content-type="article"><label>7</label><element-citation publication-type="journal"><p>Path Planning. Journal of System Simulation, 36(08): 1884–1894.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B8" content-type="article"><label>8</label><element-citation publication-type="journal"><p>Chen X, Ren G, 2020, Key Technologies and Development Trends of Intelligent Manufacturing and Robot Application. IOP Conference Series: Earth and Environmental Science, 461(1): 1–4.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B9" content-type="article"><label>9</label><element-citation publication-type="journal"><p>Patle BK, Babu LG, Pandey A, et al., 2019, A Review: On Path Planning Strategies for Navigation of Mobile Robot. Defence Technology, 15(4): 582–606.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B10" content-type="article"><label>10</label><element-citation publication-type="journal"><p>Jin S, Kou Z, Wu J, 2022, Research on Path Planning and Tracking Algorithm for Coal Mine Fan Inspection Robot. Coal Science and Technology, 50(5): 253–262.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B11" content-type="article"><label>11</label><element-citation publication-type="journal"><p>Zhao J, Feng S, Sun T, et al., 2020, Functional Design and Application of Intelligent Robot Technology in Coal-Fired Smart Power Plants. Energy Technology, 2020(4): 35–42.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B12" content-type="article"><label>12</label><element-citation publication-type="journal"><p>Liu X, Li X, Wang J, 2018, Research on Mobile Robot Path Planning Based on Improved A* Algorithm. Computer Applications and Software, 35(10): 194–199.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B13" content-type="article"><label>13</label><element-citation publication-type="journal"><p>Cai X, Xu J, Zhao F, 2019, Research on Path Planning for Intelligent Robots Based on Improved A* Algorithm. Robotics, 41(6): 809–818.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B14" content-type="article"><label>14</label><element-citation publication-type="journal"><p>Wang X, Wang S, Wang X, 2017, Path Planning Optimization Based on Genetic Algorithm and A* Algorithm. Computer Engineering and Design, 38(7): 1679–1684.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B15" content-type="article"><label>15</label><element-citation publication-type="journal"><p>Han J, Zhang Y, Sun X, 2020, Path Planning Research Based on Improved Dijkstra Algorithm. Automation Technology and Application, 39(2): 102–106.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B16" content-type="article"><label>16</label><element-citation publication-type="journal"><p>Chen X, Zhu D, Tian F, 2021, A Robot Path Planning Method Based on Dynamic Weight A* Algorithm. Robotics Technology and Applications, 46(2): 40–47.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B17" content-type="article"><label>17</label><element-citation publication-type="journal"><p>Wang J, Zhang H, Li Y, 2021, Optimization of Path Planning Based on Genetic Algorithm and A* Algorithm. Computer Science and Exploration, 15(6): 1122–131.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
