The use of deep learning technology to enhance the target recognition and attack decision-making capabilities of unmanned ground vehicles (UGVs) is an important research direction in the current military and civilian fields. The following are the paths and key considerations for technology realization： --- ### **1. Target recognition enhancement** ####**Technical solution：** -**Multi-modal sensor fusion** Combining visible light camera, infrared, LiDAR and radar data, through deep learning (such as **multimodal Transformer** or **cross-modal attention mechanism**) to improve the accuracy of target detection in complex environments (such as night, smoke). *Example model*: The hybrid architecture of YOLOv7+PointNet++ processes image and point cloud data. -**Small sample learning and domain adaptation** Use meta-learning (MAML)** or ** to generate adversarial networks (GAN)** to simulate scarce battlefield scene data to solve the problem of insufficient real data. -**Real-time optimization** Lightweight networks (such as MobileNetV3, EconomentNet) combined with model pruning/quantification technology are used to achieve low-latency inference on edge computing devices (such as NVIDIA Jetson AGX Orin). --- ### **2. Attack decision optimization** ####**Technical solution：** -**Reinforcement Learning (RL) Framework** Construct a decision-making system based on **PPO (near-end strategy optimization)**or**multi-agent RL**, simulate confrontation scenarios (such as dynamic target tracking, firepower distribution), and optimize attack timing and path planning. *Key challenges*: Design reasonable reward functions (such as enemy threat value, own ammunition consumption, mission priority). -**Man-machine collaboration mechanism** Through **Interpretable AI (XAI)**technology (such as LIME or SHAP) to provide operators with a basis for decision-making, retaining the ultimate control of humans to comply with ethical and legal requirements. -**Defense against samples** Integrate **Adversarial Training** and **input purification (such as AutoEncoder denoising)** to prevent enemy deceptive attacks on the perception system. --- ### **3. System integration and verification** -**Simulation test platform** Use Gazebo+ROS or MATLAB/Simulink to build a high-fidelity virtual battlefield, train the model and then move to the physical UGV. -**Modular design** Decouple the perception, decision-making, and control modules to facilitate upgrades (such as replacing more efficient detection algorithms) and fault isolation. --- ### **4. Ethics and safety restrictions** -**Compliance** It is necessary to follow international regulations such as the Convention on Certain Conventional Weapons to avoid fully autonomous attack systems (human presence is required). -**Robustness certification** Ensure the reliability of the system under extreme conditions through **Formal verification**or **adversarial testing**. --- ###**Conclusion** Deep learning can significantly improve the combat effectiveness of UGV, but it needs to balance technical performance with ethical constraints. It is recommended to give priority to the development of **Assisted decision-making system** instead of completely autonomous attacks, while strengthening sensor redundancy and network security protection. Military applications should be strictly controlled, and civilian fields (such as EOD robots) can land faster.