Multi-UAV Cooperative Task Assignment Based on Half Random Q-Learning
Unmanned aerial vehicle (UAV) clusters usually face problems such as complex environments, heterogeneous combat subjects, and realistic interference factors in the course of mission assignment. In order to reduce resource consumption and improve the task execution rate, it is very important to devel...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/13/12/2417 |
| _version_ | 1797500319748325376 |
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| author | Pengxing Zhu Xi Fang |
| author_facet | Pengxing Zhu Xi Fang |
| author_sort | Pengxing Zhu |
| collection | DOAJ |
| description | Unmanned aerial vehicle (UAV) clusters usually face problems such as complex environments, heterogeneous combat subjects, and realistic interference factors in the course of mission assignment. In order to reduce resource consumption and improve the task execution rate, it is very important to develop a reasonable allocation plan for the tasks. Therefore, this paper constructs a heterogeneous UAV multitask assignment model based on several realistic constraints and proposes an improved half-random Q-learning (HR Q-learning) algorithm. The algorithm is based on the Q-learning algorithm under reinforcement learning, and by changing the way the Q-learning algorithm selects the next action in the process of random exploration, the probability of obtaining an invalid action in the random case is reduced, and the exploration efficiency is improved, thus increasing the possibility of obtaining a better assignment scheme, this also ensures symmetry and synergy in the distribution process of the drones. Simulation experiments show that compared with Q-learning algorithm and other heuristic algorithms, HR Q-learning algorithm can improve the performance of task execution, including the ability to improve the rationality of task assignment, increasing the value of gains by 12.12%, this is equivalent to an average of one drone per mission saved, and higher success rate of task execution. This improvement provides a meaningful attempt for UAV task assignment. |
| first_indexed | 2024-03-10T03:00:13Z |
| format | Article |
| id | doaj.art-9ef9ff8a1804492a8fa659ac0fa2710d |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T03:00:13Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-9ef9ff8a1804492a8fa659ac0fa2710d2023-11-23T10:46:56ZengMDPI AGSymmetry2073-89942021-12-011312241710.3390/sym13122417Multi-UAV Cooperative Task Assignment Based on Half Random Q-LearningPengxing Zhu0Xi Fang1School of Science, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Science, Wuhan University of Technology, Wuhan 430070, ChinaUnmanned aerial vehicle (UAV) clusters usually face problems such as complex environments, heterogeneous combat subjects, and realistic interference factors in the course of mission assignment. In order to reduce resource consumption and improve the task execution rate, it is very important to develop a reasonable allocation plan for the tasks. Therefore, this paper constructs a heterogeneous UAV multitask assignment model based on several realistic constraints and proposes an improved half-random Q-learning (HR Q-learning) algorithm. The algorithm is based on the Q-learning algorithm under reinforcement learning, and by changing the way the Q-learning algorithm selects the next action in the process of random exploration, the probability of obtaining an invalid action in the random case is reduced, and the exploration efficiency is improved, thus increasing the possibility of obtaining a better assignment scheme, this also ensures symmetry and synergy in the distribution process of the drones. Simulation experiments show that compared with Q-learning algorithm and other heuristic algorithms, HR Q-learning algorithm can improve the performance of task execution, including the ability to improve the rationality of task assignment, increasing the value of gains by 12.12%, this is equivalent to an average of one drone per mission saved, and higher success rate of task execution. This improvement provides a meaningful attempt for UAV task assignment.https://www.mdpi.com/2073-8994/13/12/2417task allocationhalf-random Q-learningUAV collaborationrandom exploration |
| spellingShingle | Pengxing Zhu Xi Fang Multi-UAV Cooperative Task Assignment Based on Half Random Q-Learning Symmetry task allocation half-random Q-learning UAV collaboration random exploration |
| title | Multi-UAV Cooperative Task Assignment Based on Half Random Q-Learning |
| title_full | Multi-UAV Cooperative Task Assignment Based on Half Random Q-Learning |
| title_fullStr | Multi-UAV Cooperative Task Assignment Based on Half Random Q-Learning |
| title_full_unstemmed | Multi-UAV Cooperative Task Assignment Based on Half Random Q-Learning |
| title_short | Multi-UAV Cooperative Task Assignment Based on Half Random Q-Learning |
| title_sort | multi uav cooperative task assignment based on half random q learning |
| topic | task allocation half-random Q-learning UAV collaboration random exploration |
| url | https://www.mdpi.com/2073-8994/13/12/2417 |
| work_keys_str_mv | AT pengxingzhu multiuavcooperativetaskassignmentbasedonhalfrandomqlearning AT xifang multiuavcooperativetaskassignmentbasedonhalfrandomqlearning |