A Path Planning Algorithm of Inspection Robots for Solar Power Plants Based on Improved RRT
In order to improve the safety and efficiency of inspection robots for solar power plants, the Rapidly Exploring Random Tree Star (RRT*) algorithm is studied and an improved method based on an adaptive target bias and heuristic circular sampling is proposed. Firstly, in response to the problem of sl...
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| Veröffentlicht in: | Electronics (Basel) 2023-11, Vol.12 (21), p.4455 |
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| creator | Wang, Fangbin Gao, Yefei Chen, Zhong Gong, Xue Zhu, Darong Cong, Wanlin |
| description | In order to improve the safety and efficiency of inspection robots for solar power plants, the Rapidly Exploring Random Tree Star (RRT*) algorithm is studied and an improved method based on an adaptive target bias and heuristic circular sampling is proposed. Firstly, in response to the problem of slow search speed caused by random samplings in the traditional RRT* algorithm, an adaptive target bias function is applied to adjust the generation of sampling points in real-time, which continuously expands the random tree towards the target point. Secondly, to solve the problem that the RRT* algorithm has a low search efficiency and stability in narrow and long channels of solar power plants, the strategy of heuristic circular sampling combined with directional deviation is designed to resample nodes located on obstacles to generate more valid nodes. Then, considering the turning range of the inspection robot, our method will prune nodes on the paths that fail to meet constraint of the minimum turning radius. Finally, the B-spline curve is used to fit and smooth the path. A simulation experiment based on the environment of solar power plant is conducted and the result demonstrates that, compared with the RRT*, the improved RRT* algorithm reduces the search time, iterations, and path cost by 62.06%, 45.17%, and 1.6%, respectively, which provides a theoretical basis for improving the operational efficiency of inspection robots for solar power plants. |
| doi_str_mv | 10.3390/electronics12214455 |
| format | Article |
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Firstly, in response to the problem of slow search speed caused by random samplings in the traditional RRT* algorithm, an adaptive target bias function is applied to adjust the generation of sampling points in real-time, which continuously expands the random tree towards the target point. Secondly, to solve the problem that the RRT* algorithm has a low search efficiency and stability in narrow and long channels of solar power plants, the strategy of heuristic circular sampling combined with directional deviation is designed to resample nodes located on obstacles to generate more valid nodes. Then, considering the turning range of the inspection robot, our method will prune nodes on the paths that fail to meet constraint of the minimum turning radius. Finally, the B-spline curve is used to fit and smooth the path. A simulation experiment based on the environment of solar power plant is conducted and the result demonstrates that, compared with the RRT*, the improved RRT* algorithm reduces the search time, iterations, and path cost by 62.06%, 45.17%, and 1.6%, respectively, which provides a theoretical basis for improving the operational efficiency of inspection robots for solar power plants.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics12214455</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Adaptive algorithms ; Adaptive sampling ; Algorithms ; B spline functions ; Bias ; Control systems ; Heuristic ; Inspection ; Nodes ; Optimization techniques ; Path planning ; Power plants ; Robots ; Searching ; Sliding friction ; Solar energy ; Solar power plants ; Unmanned aerial vehicles ; Vehicles ; Wheels</subject><ispartof>Electronics (Basel), 2023-11, Vol.12 (21), p.4455</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c361t-5f24e58cb011d21fc56ec767fcfe93338e043b21090a2888ab6bb21b62b476e83</citedby><cites>FETCH-LOGICAL-c361t-5f24e58cb011d21fc56ec767fcfe93338e043b21090a2888ab6bb21b62b476e83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><creatorcontrib>Wang, Fangbin</creatorcontrib><creatorcontrib>Gao, Yefei</creatorcontrib><creatorcontrib>Chen, Zhong</creatorcontrib><creatorcontrib>Gong, Xue</creatorcontrib><creatorcontrib>Zhu, Darong</creatorcontrib><creatorcontrib>Cong, Wanlin</creatorcontrib><title>A Path Planning Algorithm of Inspection Robots for Solar Power Plants Based on Improved RRT</title><title>Electronics (Basel)</title><description>In order to improve the safety and efficiency of inspection robots for solar power plants, the Rapidly Exploring Random Tree Star (RRT*) algorithm is studied and an improved method based on an adaptive target bias and heuristic circular sampling is proposed. Firstly, in response to the problem of slow search speed caused by random samplings in the traditional RRT* algorithm, an adaptive target bias function is applied to adjust the generation of sampling points in real-time, which continuously expands the random tree towards the target point. Secondly, to solve the problem that the RRT* algorithm has a low search efficiency and stability in narrow and long channels of solar power plants, the strategy of heuristic circular sampling combined with directional deviation is designed to resample nodes located on obstacles to generate more valid nodes. Then, considering the turning range of the inspection robot, our method will prune nodes on the paths that fail to meet constraint of the minimum turning radius. Finally, the B-spline curve is used to fit and smooth the path. A simulation experiment based on the environment of solar power plant is conducted and the result demonstrates that, compared with the RRT*, the improved RRT* algorithm reduces the search time, iterations, and path cost by 62.06%, 45.17%, and 1.6%, respectively, which provides a theoretical basis for improving the operational efficiency of inspection robots for solar power plants.</description><subject>Adaptive algorithms</subject><subject>Adaptive sampling</subject><subject>Algorithms</subject><subject>B spline functions</subject><subject>Bias</subject><subject>Control systems</subject><subject>Heuristic</subject><subject>Inspection</subject><subject>Nodes</subject><subject>Optimization techniques</subject><subject>Path planning</subject><subject>Power plants</subject><subject>Robots</subject><subject>Searching</subject><subject>Sliding friction</subject><subject>Solar energy</subject><subject>Solar power plants</subject><subject>Unmanned aerial vehicles</subject><subject>Vehicles</subject><subject>Wheels</subject><issn>2079-9292</issn><issn>2079-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptUE1PAjEQbYwmEuQXeGniebHt7OdxJX6QkEgQTx423TKFkqXFdtH47y3iwYMzh3kzee9N8gi55mwMULFb7FD13lmjAheCp2mWnZGBYEWVVKIS53_wJRmFsGWxKg4lsAF5q-lc9hs676S1xq5p3a2dN_1mR52mUxv20dw4SxeudX2g2nn64jrp6dx9ov_RxfOdDLiikTbd7b37iHixWF6RCy27gKPfOSSvD_fLyVMye36cTupZoiDnfZJpkWJWqpZxvhJcqyxHVeSFVhorACiRpdAKziomRVmWss3buLa5aNMixxKG5ObkG1-_HzD0zdYdvI0vmyOfCxBMRNb4xFrLDhtjteu9VLFXuDPKWdQm3uuiEBkIDmkUwEmgvAvBo2723uyk_2o4a47JN_8kD9-kFng2</recordid><startdate>20231101</startdate><enddate>20231101</enddate><creator>Wang, Fangbin</creator><creator>Gao, Yefei</creator><creator>Chen, Zhong</creator><creator>Gong, Xue</creator><creator>Zhu, Darong</creator><creator>Cong, Wanlin</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20231101</creationdate><title>A Path Planning Algorithm of Inspection Robots for Solar Power Plants Based on Improved RRT</title><author>Wang, Fangbin ; 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Firstly, in response to the problem of slow search speed caused by random samplings in the traditional RRT* algorithm, an adaptive target bias function is applied to adjust the generation of sampling points in real-time, which continuously expands the random tree towards the target point. Secondly, to solve the problem that the RRT* algorithm has a low search efficiency and stability in narrow and long channels of solar power plants, the strategy of heuristic circular sampling combined with directional deviation is designed to resample nodes located on obstacles to generate more valid nodes. Then, considering the turning range of the inspection robot, our method will prune nodes on the paths that fail to meet constraint of the minimum turning radius. Finally, the B-spline curve is used to fit and smooth the path. A simulation experiment based on the environment of solar power plant is conducted and the result demonstrates that, compared with the RRT*, the improved RRT* algorithm reduces the search time, iterations, and path cost by 62.06%, 45.17%, and 1.6%, respectively, which provides a theoretical basis for improving the operational efficiency of inspection robots for solar power plants.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/electronics12214455</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Adaptive algorithms Adaptive sampling Algorithms B spline functions Bias Control systems Heuristic Inspection Nodes Optimization techniques Path planning Power plants Robots Searching Sliding friction Solar energy Solar power plants Unmanned aerial vehicles Vehicles Wheels |
| title | A Path Planning Algorithm of Inspection Robots for Solar Power Plants Based on Improved RRT |
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