A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm
To obtain more building surface information with fewer images, an unmanned aerial vehicle (UAV) path planning method utilizing an opposition-based learning artificial bee colony (OABC) algorithm is proposed. To evaluate the obtained information, a target information entropy ratio model based on obse...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/17/4312 |
| _version_ | 1797581937885315072 |
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| author | Hao Chen Yuheng Liang Xing Meng |
| author_facet | Hao Chen Yuheng Liang Xing Meng |
| author_sort | Hao Chen |
| collection | DOAJ |
| description | To obtain more building surface information with fewer images, an unmanned aerial vehicle (UAV) path planning method utilizing an opposition-based learning artificial bee colony (OABC) algorithm is proposed. To evaluate the obtained information, a target information entropy ratio model based on observation angles is proposed, considering the observation angle constraints under two conditions: whether there is an obstacle around the target or not. To efficiently find the optimal observation angles, half of the population that is lower-quality generates bit points through opposition-based learning. The algorithm searches for better individuals near the bit points when generating new solutions. Furthermore, to prevent individuals from observing targets repeatedly from similar angles, the concept of individual abandonment probability is proposed. The algorithm can adaptively abandon similar solutions based on the relative position between the individual and the population. To verify the effectiveness of the proposed method, information acquisition experiments were conducted on real residential buildings, and the results of 3D reconstruction were analyzed. The experiment results show that while model accuracy is comparable to that of the comparison method, the number of images obtained is reduced to one-fourth of the comparison method. The operation time is significantly reduced, and 3D reconstruction efficiency is remarkably improved. |
| first_indexed | 2024-03-10T23:13:47Z |
| format | Article |
| id | doaj.art-cb1ead863c114e39943c7a498fe2fd97 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T23:13:47Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-cb1ead863c114e39943c7a498fe2fd972023-11-19T08:47:27ZengMDPI AGRemote Sensing2072-42922023-09-011517431210.3390/rs15174312A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony AlgorithmHao Chen0Yuheng Liang1Xing Meng2School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin 150006, ChinaSchool of Electronic and Information Engineering, Harbin Institute of Technology, Harbin 150006, ChinaInstitute of Defense Engineering, Academy of Military Sciences, Beijing 100036, ChinaTo obtain more building surface information with fewer images, an unmanned aerial vehicle (UAV) path planning method utilizing an opposition-based learning artificial bee colony (OABC) algorithm is proposed. To evaluate the obtained information, a target information entropy ratio model based on observation angles is proposed, considering the observation angle constraints under two conditions: whether there is an obstacle around the target or not. To efficiently find the optimal observation angles, half of the population that is lower-quality generates bit points through opposition-based learning. The algorithm searches for better individuals near the bit points when generating new solutions. Furthermore, to prevent individuals from observing targets repeatedly from similar angles, the concept of individual abandonment probability is proposed. The algorithm can adaptively abandon similar solutions based on the relative position between the individual and the population. To verify the effectiveness of the proposed method, information acquisition experiments were conducted on real residential buildings, and the results of 3D reconstruction were analyzed. The experiment results show that while model accuracy is comparable to that of the comparison method, the number of images obtained is reduced to one-fourth of the comparison method. The operation time is significantly reduced, and 3D reconstruction efficiency is remarkably improved.https://www.mdpi.com/2072-4292/15/17/4312UAVartificial bee colonyopposition-based learninginformation acquisitionpath planning |
| spellingShingle | Hao Chen Yuheng Liang Xing Meng A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm Remote Sensing UAV artificial bee colony opposition-based learning information acquisition path planning |
| title | A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm |
| title_full | A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm |
| title_fullStr | A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm |
| title_full_unstemmed | A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm |
| title_short | A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm |
| title_sort | uav path planning method for building surface information acquisition utilizing opposition based learning artificial bee colony algorithm |
| topic | UAV artificial bee colony opposition-based learning information acquisition path planning |
| url | https://www.mdpi.com/2072-4292/15/17/4312 |
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