The following are some artificial intelligence algorithms that can be used to optimize the target recognition and precision strikes of war robots： ###Target recognition algorithm 1. **Convolutional neural network (CNN)** -**Principle**： -CNN is a deep learning model specially designed to process data with grid structure (such as images). It slides on the image through the convolution kernel in the convolution layer to extract features of different levels, such as edges, textures, shapes, etc. The pooling layer is used for downsampling to reduce the amount of data and retain the main features. The fully connected layer performs tasks such as classification or regression based on the extracted features. -**Advantages of target recognition applied to war robots**： -Strong adaptability to changes in the appearance of the target. For example, enemy vehicles, weapons and equipment at different angles and under different lighting conditions, CNN can learn these changes through a large amount of training data and accurately identify the target. It can still identify targets better in complex battlefield environments, such as smoke and dust, and has a higher accuracy rate than traditional algorithms. 2. **Recurrent neural network (RNN) and its variants** -**Principle**： -RNN can process sequence data and capture time series information in the data through cyclic connections between hidden layers. The short- and long-term memory network (LSTM) and the gated loop unit (GRU) are improved variants of RNN. They solve the problem of gradient disappearance or explosion in traditional RNN through a special gate structure, and can better handle long-term dependent information. -**Advantages of target recognition applied to war robots**： -Suitable for processing target information with dynamic changes, such as moving people or vehicles. For the trajectory and behavior patterns of targets on the battlefield, RNN can analyze and predict based on time series data. For example, by processing the motion attitude, speed and other information of the target in a series of image frames, it is determined whether it has the intention of attack or whether it is an important enemy target, so as to more accurately identify the target and make decisions. 3. **Algorithm based on feature extraction and matching** -**Principle**： -First extract the key characteristics of the target, such as geometric characteristics (shape, size, etc.) and physical characteristics (material reflection characteristics, etc.). Then the target matching is performed by calculating the similarity between the extracted features and the pre-stored template features. For example, the scale-invariant feature transformation (SIFT) algorithm is used to extract the feature points of the target in the image and its descriptors, and determine whether it is the same target by comparing the distances of the descriptors. -**Advantages of target recognition applied to war robots**： -The calculation is relatively simple, the hardware requirements are low, and it can run quickly on some resource-limited war robot platforms. It can quickly exclude obviously mismatched targets. For some specific types of targets with obvious characteristics, such as military equipment with unique shapes, it can be efficiently and accurately identified, and the template library can be updated to adapt to a certain degree of improvement or change in the target. ### Precision strike algorithm 1. **Strengthen learning algorithms** -**Principle**： -Agents (war robots) continue to try different actions in the environment, and learn the optimal strategy based on the reward signals fed back by the environment. Through interaction with the environment, agents gradually learn how to choose actions to maximize long-term cumulative rewards. For example, when a war robot approaches a target, it receives rewards based on sensor feedback (such as distance, angle, etc.) and strike effect (whether it hits the target, hit accuracy, etc.), so as to adjust its own trajectory, shooting parameters and other action strategies. Strategies. -**Advantages of precision strikes applied to war robots**： -Ability to optimize strike strategies in real time in a dynamic environment. The battlefield situation is changing rapidly. Intensive learning allows war robots to automatically adjust their strike methods based on various situations they actually encounter, such as sudden movement of targets and the emergence of new interfering factors, so as to continuously improve the accuracy of strikes. It does not need to know all the information of the target accurately in advance, but adapts to the complex battlefield environment through continuous experimentation and learning to achieve more accurate strikes. 2. **Particle swarm optimization algorithm (PSO)** -**Principle**： -A group of particles search for the optimal solution in the solution space, and each particle represents a possible solution (such as a combination of combat parameters). Particles update their position based on their own historical optimal position and the historical optimal position of other particles in the group, and find the optimal solution through continuous iteration. In the precision strike of war robots, particles can represent a combination of parameters such as shooting angle, launch speed, and ammunition type. The PSO algorithm is used to find the combination of parameters that can make the strike the most accurate. -**Advantages of precision strikes applied to war robots**： -You can quickly find a better solution among the many possible combinations of combat parameters. In terms of complex ballistic calculation and strike parameter optimization, the PSO algorithm can search for near-optimal parameter settings in a relatively short period of time to improve the accuracy of the strike. It has relatively low requirements for the objective function and does not require the objective function to have strict mathematical properties.