Optimization Method for Multi-UAV Dynamic Task Allocation Based on LSTM

To address the insufficiency of timeliness and accuracy in multi-UAV task allocation under dynamic environments, this paper proposes an intelligent decisionmaking optimization method based on Long Short-Term Memory (LSTM) networks. By analyzing task types and allocation constraints, the task allocation problem is transformed into an optimization problem. A four-layer model (input layer, LSTM layer, fully connected layer, output layer) is constructed, leveraging LSTM's gating mechanism to capture temporal dependencies between tasks and UAV states, thereby achieving precise matching of task priorities and UAV capabilities. Experiments show that compared to traditional RNN, the proposed method improves task allocation accuracy by 12.3% (reaching 94.0%), reduces average decision time to 0.89 seconds, and achieves a task completion rate of 96.8%, providing an effective technical approach for multi-UAV collaboration in complex dynamic scenarios.