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Aiming at the problems of smoothness and efficiency in current path planning methods, based on jump point search (JPS), a path planning method considering both smoothness and search efficiency was proposed, and the trajectory was optimized by polynomial. Firstly, in order to improve the smoothness of the path, two optimization objectives were proposed to optimize the path sequence, that was, each corner of the path was on the vertex of the obstacle grid, and the obstacles that contacted with the path corner were located on the side with the angle less than 180 degrees. Then, the search rules of JPS were improved to get more valuable paths, and each path was smoothed, and then the path selection was made by weighing the length and angle with certain rules. Finally, polynomial was used to optimize the path, and the time allocation of the trajectory optimization method was studied to speed up the iterative efficiency. The feasibility and effectiveness of this method were proved by simulation and comparison with other algorithms. The results showed that the method had the advantages of high efficiency of JPS algorithm, and effectively solved the problems of redundant points and frequent turning points in path planning of JPS algorithm. In different density environments, compared with the smoothed JPS algorithm, the path planning method reduced the path length by 0.48%~1.80%, and the cumulative turning angle was decreased by 16.93%~52.75%. In the large environment, the proposed method had similar smoothness to Lazy-Theta*, but it had higher search efficiency. The cosine function was used to allocate time to accelerate the iterative efficiency of trajectory optimization, and good results were obtained through experiments.