When using traditional autonomous navigation methods with wheeled unmanned vehicles, the algorithms do not fully account for non-complete constraint properties and steering geometry limitations. This often leads to untraceable trajectories, preventing the vehicle from completing the expected navigation tasks. In this work, a new autonomous navigation method has been designed based on the Robot Operating System (ROS) modularization, which employs the Cartographer algorithm based on graph optimization to achieve the functions of map building and localization, and incorporates SBPL_Planner and Lattice_Planner based on the search-based planning library (SBPL). Lattice_Planner together with TEB_Local_Planner based on time elastic band (TEB) planning library,are used for global and local path planning respectively, thereby faciliating autonomous navigation of the vehicle. Test experiments were conducted, and the kinematic characteristics of the vehicle were compared with those of traditional autonomous navigation methods. The results show that our autonomous navigation method can plan a path that meets the kinematic characteristics of the vehicle, effectively responds to situations such as sudden obstacles and narrow spaces, and meets the requirements for the use of intelligent wheeled unmanned vehicles for autonomous navigation functions.