Research On Intelligent Vehicle Global Path Planning Based On Optimized S-AWA* Hybrid Algorithm

At present,intelligent vehicle is mainly composed of three modules: environment perception,path planning and intelligent control.In the path planning,the single connection mode and most heuristic algorithms have low search efficiency or poor path precision,which can not meet the requirements of the global path planning of the current intelligent vehicle.Therefore,a hybrid S-AWA* optimization algorithm of global path planning is proposed in intelligent vehicle field,which aims at improving the path accuracy while reducing the number of expanded nodes and reducing the search time.The main contents are as follows:First,S-AWA* hybrid algorithm of global path planning is proposed in intelligent vehicle field,including the node mode of hybrid connection and the S algorithm based on heuristic function.At the same time,a parallel search strategy is proposed.The strategy can select the appropriate algorithm according to the obstacles in the four directions of the current node.Subsequently,the path planning of the S-AWA* hybrid algorithm is simulated,the feasibility of S-AWA* hybrid algorithm is verified by simulations,and the problems in S-AWA* hybrid algorithm are analyzed.Then,aiming at the problem of the number of extended nodes in the S-AWA* hybrid algorithm of global path planning,the heuristic function and the search process of the S-AWA* hybrid algorithm are optimized,that is,the S-AWA* hybrid optimization algorithm is proposed.In the aspect of heuristic function,the dynamic optimization factor is introduced.At the same time,a new dynamic evaluation function is put forward,and the parameters of the proposed various parameters are optimized.In the search process,the limited termination coefficient is introduced and the termination condition of the four connection AWA* algorithm is modified;then the path planning simulations are carried out,Simulation texts verify the effectiveness of the S-AWA* hybrid optimization algorithm.Finally,the real environment map construction and multi path planning simulation texts are carried out,the consistency of the text results and the simulation results is verified in S-AWA* hybrid optimization algorithm,thus the effectiveness and superiority of the S-AWA* hybrid optimization algorithm are further explained.The research shows that S-AWA* hybrid algorithm can reduce the number of extended nodes and reduce the time consuming of the search while improving the path precision.Compared to the traditional AWA* algorithm,the path length,the number of extended nodes and the time consuming are respectively 14.2%,43.6% and 54.7%.the S-AWA * hybrid optimization algorithm is compared to the S-AWA* hybrid algorithm,while maintaining the same path length,the number of extended nodes and search time are reduced by 10.6% and 7.1% respectively.The contradiction problem that the path accuracy and search efficiency of intelligent vehicle global path planning can not be met at the same time under the complex environment map is effectively solved.