Autonomous Tracking of ShenZhou Reentry Capsules Based on Heterogeneous UAV Swarms
The safe landing and rapid recovery of the reentry capsules are very important to manned spacecraft missions. A variety of uncertain factors, such as flight control accuracy and wind speed, lead to a low orbit prediction accuracy and a large landing range of reentry capsules. It is necessary to real...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/7/1/20 |
| _version_ | 1797443619616980992 |
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| author | Boxin Li Boyang Liu Dapeng Han Zhaokui Wang |
| author_facet | Boxin Li Boyang Liu Dapeng Han Zhaokui Wang |
| author_sort | Boxin Li |
| collection | DOAJ |
| description | The safe landing and rapid recovery of the reentry capsules are very important to manned spacecraft missions. A variety of uncertain factors, such as flight control accuracy and wind speed, lead to a low orbit prediction accuracy and a large landing range of reentry capsules. It is necessary to realize the autonomous tracking and continuous video observation of the reentry capsule during the low-altitude phase. Aiming at the Shenzhou return capsule landing mission, the paper proposes a new approach for the autonomous tracking of Shenzhou reentry capsules based on video detection and heterogeneous UAV swarms. A multi-scale video target detection algorithm based on deep learning is developed to recognize the reentry capsules and obtain positioning data. A self-organizing control method based on virtual potential field is proposed to realize the cooperative flight of UAV swarms. A hardware-in-the-loop simulation system is established to verify the method. The results show that the reentry capsule can be detected in four different states, and the detection accuracy rate of the capsule with parachute is 99.5%. The UAV swarm effectively achieved autonomous tracking for the Shenzhou reentry capsule based on the position obtained by video detection. This is of great significance in the real-time searching of reentry capsules and the guaranteeing of astronauts’ safety. |
| first_indexed | 2024-03-09T12:59:40Z |
| format | Article |
| id | doaj.art-4884cef7001d4691a0a25bfa8222d251 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-09T12:59:40Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-4884cef7001d4691a0a25bfa8222d2512023-11-30T21:55:10ZengMDPI AGDrones2504-446X2022-12-01712010.3390/drones7010020Autonomous Tracking of ShenZhou Reentry Capsules Based on Heterogeneous UAV SwarmsBoxin Li0Boyang Liu1Dapeng Han2Zhaokui Wang3School of Aerospace Engineering, Tsinghua University, Beijing 100084, ChinaSchool of Aerospace Engineering, Tsinghua University, Beijing 100084, ChinaSchool of Aerospace Engineering, Tsinghua University, Beijing 100084, ChinaSchool of Aerospace Engineering, Tsinghua University, Beijing 100084, ChinaThe safe landing and rapid recovery of the reentry capsules are very important to manned spacecraft missions. A variety of uncertain factors, such as flight control accuracy and wind speed, lead to a low orbit prediction accuracy and a large landing range of reentry capsules. It is necessary to realize the autonomous tracking and continuous video observation of the reentry capsule during the low-altitude phase. Aiming at the Shenzhou return capsule landing mission, the paper proposes a new approach for the autonomous tracking of Shenzhou reentry capsules based on video detection and heterogeneous UAV swarms. A multi-scale video target detection algorithm based on deep learning is developed to recognize the reentry capsules and obtain positioning data. A self-organizing control method based on virtual potential field is proposed to realize the cooperative flight of UAV swarms. A hardware-in-the-loop simulation system is established to verify the method. The results show that the reentry capsule can be detected in four different states, and the detection accuracy rate of the capsule with parachute is 99.5%. The UAV swarm effectively achieved autonomous tracking for the Shenzhou reentry capsule based on the position obtained by video detection. This is of great significance in the real-time searching of reentry capsules and the guaranteeing of astronauts’ safety.https://www.mdpi.com/2504-446X/7/1/20reentry capsulesautonomous trackingUAV swarm flight controlvideo detection |
| spellingShingle | Boxin Li Boyang Liu Dapeng Han Zhaokui Wang Autonomous Tracking of ShenZhou Reentry Capsules Based on Heterogeneous UAV Swarms Drones reentry capsules autonomous tracking UAV swarm flight control video detection |
| title | Autonomous Tracking of ShenZhou Reentry Capsules Based on Heterogeneous UAV Swarms |
| title_full | Autonomous Tracking of ShenZhou Reentry Capsules Based on Heterogeneous UAV Swarms |
| title_fullStr | Autonomous Tracking of ShenZhou Reentry Capsules Based on Heterogeneous UAV Swarms |
| title_full_unstemmed | Autonomous Tracking of ShenZhou Reentry Capsules Based on Heterogeneous UAV Swarms |
| title_short | Autonomous Tracking of ShenZhou Reentry Capsules Based on Heterogeneous UAV Swarms |
| title_sort | autonomous tracking of shenzhou reentry capsules based on heterogeneous uav swarms |
| topic | reentry capsules autonomous tracking UAV swarm flight control video detection |
| url | https://www.mdpi.com/2504-446X/7/1/20 |
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