Intelligent vehicle path planning based on fusion algorithm

Aiming at the problem of intelligent vehicle path planning in complex environment, a fusion algorithm is proposed, which combined the improved artificial potential field algorithm and ant colony algorithm. First, improve the target unreachable problem of traditional APF, then use the planning result of the improved APF as the optimization basis of ACO, add the distance from the intelligent vehicle to the next node, use global and local hybrid update, and finally use three B samples Curve planning for route optimization. It not only solves the local minimum problem, but also plans the optimal global path, so that the intelligent vehicle can effectively avoid obstacles on the road and drive more safely. The simulation experiment is carried out in MATLAB software, and the algorithm in this paper is compared with the algorithm in literature [22]. Experimental results show that the fusion algorithm reduces the blindness and improves the efficiency of path search.