In the field of military reconnaissance, the path planning and target recognition of UAV clusters are key technologies, and the application of machine learning technology can significantly improve their efficiency and accuracy. The following are the specific application directions and technical paths： --- ### **1. Path planning optimization** ####**(1) Dynamic environment modeling and real-time obstacle avoidance** -**Reinforcement learning (RL)**: Through algorithms such as Q-learning and deep deterministic strategy gradient (DDPG), drones are trained to autonomously avoid obstacles and dynamically adjust their paths in complex environments (such as mountains and cities). For example, combining LIDAR and visual data to update environmental maps in real time. -**Multi-agent collaboration (MARL)**: Use multi-agent reinforcement learning (such as MADDPG) to realize cluster collaborative path planning, avoid collisions and optimize overall coverage. For example, distributed decision-making balances reconnaissance efficiency and concealment. ####**(2)Robustness in confrontational scenarios** -**Confrontation generation network (GAN)**: Simulates enemy interference (such as GPS spoofing, electromagnetic suppression) to generate confrontation samples, and trains drones to maintain path stability under interference. -**Meta-Learning**: Quickly adapt to the new battlefield environment and reduce retraining time. ####**(3)Energy and time optimization** -**Combination of genetic algorithm (GA) and neural network**: Optimize the Pareto frontier solution for multi-objective (shortest path, lowest energy consumption, and maximum reconnaissance coverage). --- ### **2. Target recognition enhancement** ####**(1) Multi-modal data fusion** -**Convolutional neural network (CNN)**: Processes visible/infrared images to identify camouflage targets (such as combining thermal imaging with visible light data). -**Transformer model**: Analyze time series data (such as video streams) to improve the tracking accuracy of dynamic targets (mobile vehicles, personnel). ####**(2) Small sample learning (Few-shot Learning)** -Migration learning**: Pre-trained models (such as ResNet) are fine-tuned with a small amount of labeled data to adapt to new target types (such as new equipment). -**Siamese network**: Identify similar targets (such as different models of tanks) through comparative learning. ####**(3) Robust identification in the adversarial environment** -Adversarial Training (Adversarial Training)**: Defend against enemy interference with image recognition (such as adding noise, camouflage patterns). --- ### **3. Cluster collaboration and decision-making** - **Federated Learning**: After each drone trains the model locally, the global model is aggregated to protect data privacy and enhance group intelligence. - **Graph neural network (GNN)**: Model the communication topology between drones and optimize information sharing strategies (such as prioritizing the transmission of high-value target data). --- ### **4. Practical challenges and countermeasures** -**Real-time**: The combination of lightweight models (such as MobileNet) and edge computing (airborne AI chip). -**Data scarcity**: Synthetic data generation (such as Sim2Real technology) and real data mixed training. -**Confrontational threats**: Continuous Online Learning (Continuous Learning) to deal with new types of interference. --- ### **5. Military application cases** - **Reconnaissance-strike integration**: After the cluster identifies high-value targets, it assigns attack tasks in real time (it needs to be linked with the command system). -Electronic warfare environment survival**: Training drones to switch communication frequency bands or relay nodes under strong electromagnetic interference through RL. --- ###**Conclusion** Machine learning technology can significantly improve the autonomy, adaptability and survivability of uav clusters, but it needs to be customized in accordance with military needs (such as concealment and anti-interference). Future directions include the exploration of bionic algorithms (such as ant colony optimization) and quantum machine learning to cope with more complex battlefield environments.