Abstract: Abstract: The urban battlefield is the main position of conventional warfare and daily security， and excellent urban battlefield penetration capabilities can help our fighters better and faster complete reconnaissance， strike， rescue and other tasks. However， the complex street environment in the city， and the possibility of interception by enemy targets， make the urban battlefield environment complex and changeable， greatly increasing the difficulty of completing the mission. Traditional path planning methods rely on accurate static maps and rule constraints， and lack flexibility and adaptability. Therefore， this paper proposes an autonomous navigation method for intelligent vehicles in urban battlefield， and designs discrete action spaces and reward functions based on task completion. Firstly， this paper takes the urban battlefield penetration task as an example to design the state space and action space， and selects a suitable deep reinforcement learning algorithm. Then， based on Gazebo simulation platform and ROS， the algorithm flow framework and experimental scheme are designed. The experimental results show that the intelligent car using this method in the urban battlefield environment can effectively pass through obstacles and avoid enemy units to reach the designated place， which improves the success rate of penetration.

Key words: Key words: urban battlefield, path planning, deep reinforcement learning, autonomous navigation