Aiming at the problem that the path planned by the hybrid A* algorithm is not smooth enough and low efficiency, the evaluation function of the hybrid A* algorithm is optimized, the method of Angle penalty coefficient is introduced to make the path smoother, and the method of node expansion with optimal step length is introduced to improve the search efficiency. Firstly, A hybrid A* algorithm is combined with TEB (Timed Elastic Band) algorithm to generate a hybrid path planning algorithm. Then, the path planning process of Ackermann's steering Robot is simulated based on ROS (Robot Operating System).The results show that the average distance of path planning using mixed A* algorithm and DWA algorithm is 23.83% less than that using A* algorithm and DWA algorithm. The average path planning distance using the mixed A* algorithm and TEB algorithm is 22.49% less than that using the A* algorithm and TEB algorithm. The average path planning time using A* algorithm and TEB algorithm is 6.99% less than that using A* algorithm and DWA algorithm. The average path planning time using the mixed A* algorithm and TEB algorithm is 6.25% less than that using the mixed A* algorithm and DWA algorithm.The path planning efficiency of hybrid A* algorithm combined with TEB algorithm is higher and the planned path is shorter, which proves the effectiveness and superiority of hybrid path planning algorithm. Finally, the path planning experiment of the Ackerman steering robot is carried out in the storage environment. Aiming at the large error between the actual trajectory and the planned trajectory of the robot, a method is proposed to estimate the pose of the obstacle by Kalman filter and track the trajectory of the robot. The results show that the tracking error of Kalman filter is less than 0.2 m.