Research On Path Planning For Obstacle Avoidance Of Vehicle Based On Improved Artificial Potential Field

With the increasing number of automobiles in the world,a series of problems such as frequent traffic accidents,road congestion and environmental energy crisis have begun to be exposed to people's attention,especially the high incidence of traffic accidents.Because of the rapid development of sensor technology and intelligent control,intelligent car been given high expectations to solve problem.Intelligent cars can reduce or eliminate driver error behavior and solve traffic safety problems.Among them,obstacle avoidance path planning is the core technology of intelligent vehicle and the precondition of solving safety problems.In this paper,the traditional artificial potential field planning algorithm is studied,the principle of traditional artificial potential field method is analyzed.The gravitational potential field model and the repulsion potential field model are established.Taking the intelligent car as the controlled object,the artificial potential field force analysis is carried out,and two self-defects of the traditional artificial potential field are proposed,the target unreachability problem and the local minimum value problem.By introducing the relative position of the intelligent vehicle and the target point into the repulsion potential field function.When the vehicle is driving towards the target point,the gravit y and repulsion force are reduced to zero at the same time,so the target unreachable problem is solved.Considering the dynamic driving environment of intelligent cars,the influence factors of relative velocity and relative acceleration bewteen intelligent cars and obstacles to the artificial potential field are added and the relative velocity and relative acceleration repulsion potential field are established.The conditions of intelligent vehicles affected by the repulsive force of obstacles are also updated,so as to make it more suitable with the actual driving situation.For the driving environment with road boundary restrictions,the road boundary repulsion potential field is added to keep the planned path in the road,and the safety of the obstacle avoidance path is improved.Based on the Ackermann steering principle of the cars,the artificial potential field method planning path is constrained to make it more in line with the driving requirements of the intelligent vehicle tracking path.A vehicle dynamics model based on control algorithm is established and a feedforward plus feedback controller is designed based on LQR optimal control for the coupling relationship between lateral position error and heading angle error of front-wheel steering vehicle in tracking path.Because the determination of state weight parameters in LQR is often based on empirical rules,a genetic algorithm with global optimization ability is proposed.The LQR performance index is used as the fitness function of the genetic algorithm to optimize the matrix parameters,so as to reduce the overshoot of the control system and increase the convergence of the control system.In order to verify the improved artificial potential field path planning and the effectiveness of the LQR-based horizontal controller,simulation and real vehicle verification were carried out.The results show that the improved artificial potential field path planning system and the LQR-based horizontal controller to achieve the requirements of intelligent vehicle autonomous obstacle avoidance.