An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship
Airship-based Earth observation is of great significance in many fields such as disaster rescue and environment monitoring. To facilitate efficient observation of high-altitude airships (HAA), a high-quality observation scheduling approach is crucial. This paper considers the scheduling of the imagi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/5/2050 |
| _version_ | 1827650524991717376 |
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| author | Jiawei Chen Qizhang Luo Guohua Wu |
| author_facet | Jiawei Chen Qizhang Luo Guohua Wu |
| author_sort | Jiawei Chen |
| collection | DOAJ |
| description | Airship-based Earth observation is of great significance in many fields such as disaster rescue and environment monitoring. To facilitate efficient observation of high-altitude airships (HAA), a high-quality observation scheduling approach is crucial. This paper considers the scheduling of the imaging sensor and proposes a hierarchical observation scheduling approach based on task clustering (SA-TC). The original observation scheduling problem of HAA is transformed into three sub-problems (i.e., task clustering, sensor scheduling, and cruise path planning) and these sub-problems are respectively solved by three stages of the proposed SA-TC. Specifically, a novel heuristic algorithm integrating an improved ant colony optimization and the backtracking strategy is proposed to address the task clustering problem. The 2-opt local search is embedded into a heuristic algorithm to solve the sensor scheduling problem and the improved ant colony optimization is also implemented to solve the cruise path planning problem. Finally, extensive simulation experiments are conducted to verify the superiority of the proposed approach. Besides, the performance of the three algorithms for solving the three sub-problems are further analyzed on instances with different scales. |
| first_indexed | 2024-03-09T20:20:19Z |
| format | Article |
| id | doaj.art-4d20f1a35205484b81b9f763287e46cf |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T20:20:19Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4d20f1a35205484b81b9f763287e46cf2023-11-23T23:50:20ZengMDPI AGSensors1424-82202022-03-01225205010.3390/s22052050An Observation Scheduling Approach Based on Task Clustering for High-Altitude AirshipJiawei Chen0Qizhang Luo1Guohua Wu2School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USASchool of Traffic & Transportation Engineering, Central South University, Changsha 410075, ChinaSchool of Traffic & Transportation Engineering, Central South University, Changsha 410075, ChinaAirship-based Earth observation is of great significance in many fields such as disaster rescue and environment monitoring. To facilitate efficient observation of high-altitude airships (HAA), a high-quality observation scheduling approach is crucial. This paper considers the scheduling of the imaging sensor and proposes a hierarchical observation scheduling approach based on task clustering (SA-TC). The original observation scheduling problem of HAA is transformed into three sub-problems (i.e., task clustering, sensor scheduling, and cruise path planning) and these sub-problems are respectively solved by three stages of the proposed SA-TC. Specifically, a novel heuristic algorithm integrating an improved ant colony optimization and the backtracking strategy is proposed to address the task clustering problem. The 2-opt local search is embedded into a heuristic algorithm to solve the sensor scheduling problem and the improved ant colony optimization is also implemented to solve the cruise path planning problem. Finally, extensive simulation experiments are conducted to verify the superiority of the proposed approach. Besides, the performance of the three algorithms for solving the three sub-problems are further analyzed on instances with different scales.https://www.mdpi.com/1424-8220/22/5/2050airshipobservation schedulingtask clusteringheuristic algorithmant colony optimization |
| spellingShingle | Jiawei Chen Qizhang Luo Guohua Wu An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship Sensors airship observation scheduling task clustering heuristic algorithm ant colony optimization |
| title | An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship |
| title_full | An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship |
| title_fullStr | An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship |
| title_full_unstemmed | An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship |
| title_short | An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship |
| title_sort | observation scheduling approach based on task clustering for high altitude airship |
| topic | airship observation scheduling task clustering heuristic algorithm ant colony optimization |
| url | https://www.mdpi.com/1424-8220/22/5/2050 |
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