Supervisor:
Southwest Ordnance Industry Bureau
Organizer:
Chongqing Ordnance Industry Society
Chongqing University of Technology
Chongqing University of Technology
A predictive maintenance decision algorithm for UAV swarms based on deep reinforcement learning
| DOI: | 10.11809/bqzbgcxb2024.11.036 |
| Keywords: | unmanned aerial vehicle swarm; predictive maintenance; deep reinforcement learning; decision and planning; reliability |
| Abstract: | Predictive maintenance is one of the key technologies to improve the reliability of unmanned aerial vehicles (UAV) swarms.The difficulty lies in the multi level structure of the UAV swarm, the uneven degradation of UAV components, and the high impact of maintenance timing.By analyzing the UAV swarm operation and maintenance process, this paper proposes a problem model for UAV swarm predictive maintenance and analyzes its NP hard characteristics.A solution framework based on deep Q network (DQN) is constructed, then the corresponding deep reinforcement learning algorithm is given.The case verification is carried out using a swarm of 10 groups and 14 UAVs in each group as an example.The case results show that the proposed method can efficiently and stably provide predictive maintenance decision making solutions during long term operation, and improve maintenance benefits in high dimensional nonlinear state action space. |
| Issue: | Vol. 45 No. 11 (2024) |
| Published: | 2024-11-30 |
| PDF HTML | |