Research On Path Planning Algorithm For Intelligent Vehicle Based On Improved Ant Colony Optimization And Artificial Potential Field Method

With the rapid development of computer information technology,the traditional car technology is not enough to meet the needs of people,it is that people want to free from the process of running car.In this context,the research of intelligent vehicle technology has been put on the agenda.And the intelligent vehicle path planning technology is one of the most important aspects in the research field of intelligent vehicles.Intelligent vehicle path planning refers to find a optimal or suboptimal and collision safety path from starting point to target point according to some optimization criteria in the environment with obstacles.In the research of this paper,the information of obstacles in the running environment of the car is known and this paper improved the artificial potential field method and ant colony algorithm,and combined the advantages of both algorithms.The intelligent vehicle can find an optimal and collision safety path from starting point to target point by use this paper’s improved method.The specific research contents include the following aspects:Firstly,this paper proposes a repulsion field function based on target point location information to solve the problem that the object nearby the obstacles cannot be reached in artificial potential field method.The traditional artificial potential field method in the case of a target with an obstacle around,due to the unreasonable potential field model,the target zero gravity,but the repulsive force is not zero,so the intelligent vehicle cannot reach the target point but hover near the target point.To solve this problem,this paper presents a repulsion function based on target point location information and make the resulting force on the intelligent vehicle at the target point to be zero.The method in this paper effectively solve the problem of the target near the obstacles and the effectiveness of this paper’s algorithm is verified by simulation.Secondly,this paper proposes an improved ant colony algorithm to solve this problem that the algorithm’s planning time is too long and low efficiency due to the lack of initial pheromone.The main improvement points are as follows:1.The potential field force is added to the ant colony algorithm as a variable heuristic factor.In the early stage of the algorithm planning,the potential field force illumination is dominant,so that the ant colony algorithm can plan the feasible path in the early stage.In the later stage,the inspiration information of the potential field force is reduced,and the dominant position is given to ant colony algorithm to optimize the later path.2.The updating rules of pheromones have been improved and the rules of information element updating have been put forward.After each iteration,the ant colony algorithm will give a high pheromone reward to the short paths and a less pheromone reward will be given to those longer paths.At the same time,we give a limitation to the pheromone in order to avoid excessive concentration of local path-pheromone.And last,the effectiveness of this paper’s algorithm is verified by simulation.Finally,summarized the work of this paper,and made a forecast of the future research direction.