一种面向城市战场的智能车自主导航方法

An Autonomous Navigation Method for Intelligent Vehicles in Urban Battlefield

城市战场是常规战争和日常治安的主要阵地,出色的城市战场突防能力能够帮助我方作战人员更好、更快地完成侦查、打击、营救等任务。然而,城市内街道环境错综复杂同时还可能存在敌方的拦截,使得城市战场环境复杂多变,大大增加了完成任务的难度。传统的路径规划方法依赖于精确的静态地图和规则约束,缺乏灵活性和适应性。因此,本文提出一种面向城市战场的智能车自主导航方法,并设计离散的动作空间和基于任务完成度的奖励函数。首先,以城市战场突防任务为例,设计状态空间、动作空间,并选择适合的深度强化学习算法;然后,基于Gazebo仿真平台和ROS设计算法流程框架和实验方案。实验结果表明,在城市战场环境下运用该方法的智能小车能够有效地穿越障碍并躲避敌方单位到达指定地点,提高了突防的成功率。

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 envi ronment 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 autono mous 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.