Application Of Tabu Search Algorithm In Driverless Path Planning

UGV(Driverless vehicle)is a kind of smart car.It can also be called a wheeled mobile robot.Its basic operation principle is that the vehicle detects the road environment information of the vehicle body and the surrounding road through its own sensing system.For example: road conditions,their own car body position and other obstacles in a certain range that may affect the vehicle's travel,with the computer system in the car,and through the coordination of artificial intelligence,visual computing,radar,monitoring devices and global positioning system Cooperate and plan a safe path so that the car can achieve autonomous driving.In order to ensure the safe driving of the vehicle and good traffic order,the unmanned vehicle must make reasonable route adjustments for complex traffic conditions during the journey,so that it can reach the target smoothly,so the reliable path planning algorithm is to improve the unmanned Key indicators for driving vehicle intelligence.This also requires the vehicle to make real time obstacle avoidance behavior based on the local road condition on the basis of the global path planning,thereby driving the driving demand for safe driving.Based on the above requirements,this paper studies the local path planning algorithm of unmanned vehicles.Firstly,the research status and development trend of unmanned driving technology are analyzed.The existing path planning algorithms are summarized and compared.On this basis,the local path planning algorithm and dynamic hedging analysis are carried out,and the improved ideas of the algorithm are proposed respectively.The simulation results verify that the optimized algorithm achieves satisfactory results in the partial path planning of the unmanned vehicle.The specific work is as follows:(1)Studying the characteristics of traditional path planning algorithms: In the comparative analysis of traditional path planning algorithms,it is found that the existing mainstream path planning algorithms are more complicated and less flexible,and they are easy to fall into local poles.Aiming at this situation,the tabu search algorithm is simple algorithm,flexible calculation and the existence of the taboo table avoids the local pole problem,so that it becomes an ideal algorithm to solve the problem of unmanned vehicle path planning.(2)Research on local path planning algorithm: An improved tabu search algorithm based on A* algorithm to determine the initial solution is proposed.The introduction of the A* algorithm to determine the initial solution provides an effective basis for the evaluation of the solution in the iterative algorithm,which ensures the efficiency of the algorithm.Through simulation experiments,it is verified that the improved algorithm has significantly improved the accuracy and effectiveness of path planning.(3)Studying the dynamic hedging of unmanned vehicles: This paper proposes a method of using model predictive control and rolling optimization,combined with the rolling window method to solve the problem of safe avoidance of sudden dynamic obstacles during vehicle driving.