Research On Route Planning Technology Of Unmanned Ammunition Supply Vehicle

The unmanned automatic ammunition supply vehicle is one of the important supporting equipment for unmanned air defense weapon systems,and is an important equipment for improving the level of unmanned,intelligent,and information technology of equipment,improving battlefield support capabilities,and shortening ammunition replenishment time.And path planning technology is the key to how unmanned ammunition supply vehicles accurately reach their destinations and successfully complete ammunition supply tasks.However,there is limited research on path planning algorithms for unmanned ammunition supply vehicles,and path planning algorithms have certain limitations and drawbacks to varying degrees.This paper takes the unmanned ammunition supply vehicle of an air defense weapon system as the research object,takes the urban warfare environment as the background,establishes the kinematics model of the unmanned ammunition supply vehicle,conducts path planning research and analysis under the known environment and the unknown environment,and proposes an improved path planning method for the unmanned ammunition supply vehicle.The main work of the paper is as follows:1.Mainly analyzed the existing environmental modeling methods and path planning research methods.Three common environment modeling methods are analyzed,and the grid map method is selected to model the urban warfare environment studied in this paper.Classify path planning,which is mainly divided into global path planning and local path planning.On this basis,appropriate path planning research methods are selected.This paper selects the improved A * algorithm as the global path planning algorithm and the improved dynamic window method as the local path planning algorithm to study them separately.2.Propose a global path planning algorithm that improves the A * algorithm.Using the grid map method for environmental modeling,the turning mechanism of the standard A *algorithm is improved in case of collision with obstacles during global path planning.At the same time,the ant colony algorithm is introduced to address issues such as longer and less smooth paths,and corresponding improvements are made to the ant colony algorithm.The improved ant colony algorithm is used to iteratively optimize the path of the standard A *algorithm.The improved A * algorithm generates a path that provides a certain safe distance for the movement of the unmanned ammunition supply vehicle,reducing the path planning length and time while ensuring safety,which is beneficial for the unmanned ammunition supply vehicle to quickly complete supply tasks.3.Propose a local path planning algorithm that improves the dynamic window method.The movement model of the unmanned ammunition supply vehicle is analyzed,and the kinematics model is established.Based on this,the dynamic window method is analyzed,and the global path planning evaluation subfunction and curvature evaluation subfunction are introduced to obtain a local path planning algorithm based on the improved dynamic window method.Conduct path planning simulations separately.Firstly,simulate the path planning algorithm under known environmental information conditions,and then simulate the path planning algorithm under unknown environmental information conditions.The feasibility of the algorithm was verified through simulation.4.Use the ROS simulation system and Gazebo software to conduct simulation experiments to verify the path planning method of unmanned ammunition supply vehicles.Through simulation experiments on the path planning of unmanned ammunition supply vehicles,the feasibility of the improved algorithm in path planning under different environmental information was verified,providing a basis for subsequent navigation research on unmanned ammunition supply vehicles.The simulation results show that the path planning method combining the improved A * algorithm and the improved DWA algorithm validates the feasibility of the algorithm in both known and unknown environmental information,enabling the unmanned ammunition supply vehicle to better complete its superior tasks in different obstacle environments.The results of this study will further enrich the research content of unmanned automatic ammunition replenishment technology,and provide a certain theoretical basis and technical support for it.