Trajectory Planning for Unmanned Vehicles on Airport Apron Under Aircraft–Vehicle–Airfield Collaboration
To address the issue of safe, orderly, and efficient operation for unmanned vehicles within the apron area in the future, a hardware framework of aircraft–vehicle–airfield collaboration and a trajectory planning method for unmanned vehicles on the apron were proposed. As for the vehicle–airfield per...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/71 |
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| author | Dezhou Yuan Yingxue Zhong Xinping Zhu Ying Chen Yue Jin Xinze Du Ke Tang Tianyu Huang |
| author_facet | Dezhou Yuan Yingxue Zhong Xinping Zhu Ying Chen Yue Jin Xinze Du Ke Tang Tianyu Huang |
| author_sort | Dezhou Yuan |
| collection | DOAJ |
| description | To address the issue of safe, orderly, and efficient operation for unmanned vehicles within the apron area in the future, a hardware framework of aircraft–vehicle–airfield collaboration and a trajectory planning method for unmanned vehicles on the apron were proposed. As for the vehicle–airfield perspective, a collaboration mechanism between flight support tasks and unmanned vehicle departure movement was constructed. As for the latter, a control mechanism was established for the right-of-way control of the apron. With the goal of reducing waiting time downstream of the pre-selected path, a multi-agent reinforcement learning model with a collaborative graph was created to accomplish path selection among various origin–destination pairs. Then, we took Apron NO.2 in Ezhou Huahu Airport as an example for simulation verification. The results show that, compared with traditional methods, the proposed method improves the average vehicle speed and reduces average vehicle queue time by 11.60% and 32.34%, respectively. The right-of-way signal-switching actions are associated with the path selection behavior of the corresponding agent, fitting the created aircraft–vehicle collaboration. After 10 episodes of training, the Q-values can steadily converge, with the deviation rate decreasing from 40% to below 0.22%, making the balance between sociality and competitiveness. A single trajectory can be planned in just 0.78 s, and for each second of training, 7.54 s of future movement of vehicles can be planned in the simulation world. Future research could focus on online rolling trajectory planning for UGSVs in the apron area, and realistic verification under multi-sensor networks can further advance the application of unmanned vehicles in apron operations. |
| format | Article |
| id | doaj-art-e6abcad206a74ffaa020906d4fcd587a |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-e6abcad206a74ffaa020906d4fcd587a2025-01-10T13:20:47ZengMDPI AGSensors1424-82202024-12-012517110.3390/s25010071Trajectory Planning for Unmanned Vehicles on Airport Apron Under Aircraft–Vehicle–Airfield CollaborationDezhou Yuan0Yingxue Zhong1Xinping Zhu2Ying Chen3Yue Jin4Xinze Du5Ke Tang6Tianyu Huang7School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, ChinaSchool of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, ChinaSchool of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, ChinaCapital Airports Holdings Co., Ltd., Beijing 101317, ChinaCapital Airports Holdings Co., Ltd., Beijing 101317, ChinaCapital Airports Holdings Co., Ltd., Beijing 101317, ChinaSchool of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, ChinaSchool of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, ChinaTo address the issue of safe, orderly, and efficient operation for unmanned vehicles within the apron area in the future, a hardware framework of aircraft–vehicle–airfield collaboration and a trajectory planning method for unmanned vehicles on the apron were proposed. As for the vehicle–airfield perspective, a collaboration mechanism between flight support tasks and unmanned vehicle departure movement was constructed. As for the latter, a control mechanism was established for the right-of-way control of the apron. With the goal of reducing waiting time downstream of the pre-selected path, a multi-agent reinforcement learning model with a collaborative graph was created to accomplish path selection among various origin–destination pairs. Then, we took Apron NO.2 in Ezhou Huahu Airport as an example for simulation verification. The results show that, compared with traditional methods, the proposed method improves the average vehicle speed and reduces average vehicle queue time by 11.60% and 32.34%, respectively. The right-of-way signal-switching actions are associated with the path selection behavior of the corresponding agent, fitting the created aircraft–vehicle collaboration. After 10 episodes of training, the Q-values can steadily converge, with the deviation rate decreasing from 40% to below 0.22%, making the balance between sociality and competitiveness. A single trajectory can be planned in just 0.78 s, and for each second of training, 7.54 s of future movement of vehicles can be planned in the simulation world. Future research could focus on online rolling trajectory planning for UGSVs in the apron area, and realistic verification under multi-sensor networks can further advance the application of unmanned vehicles in apron operations.https://www.mdpi.com/1424-8220/25/1/71air transportationvehicle trajectory planningmulti-agent reinforcement learning |
| spellingShingle | Dezhou Yuan Yingxue Zhong Xinping Zhu Ying Chen Yue Jin Xinze Du Ke Tang Tianyu Huang Trajectory Planning for Unmanned Vehicles on Airport Apron Under Aircraft–Vehicle–Airfield Collaboration Sensors air transportation vehicle trajectory planning multi-agent reinforcement learning |
| title | Trajectory Planning for Unmanned Vehicles on Airport Apron Under Aircraft–Vehicle–Airfield Collaboration |
| title_full | Trajectory Planning for Unmanned Vehicles on Airport Apron Under Aircraft–Vehicle–Airfield Collaboration |
| title_fullStr | Trajectory Planning for Unmanned Vehicles on Airport Apron Under Aircraft–Vehicle–Airfield Collaboration |
| title_full_unstemmed | Trajectory Planning for Unmanned Vehicles on Airport Apron Under Aircraft–Vehicle–Airfield Collaboration |
| title_short | Trajectory Planning for Unmanned Vehicles on Airport Apron Under Aircraft–Vehicle–Airfield Collaboration |
| title_sort | trajectory planning for unmanned vehicles on airport apron under aircraft vehicle airfield collaboration |
| topic | air transportation vehicle trajectory planning multi-agent reinforcement learning |
| url | https://www.mdpi.com/1424-8220/25/1/71 |
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