Implementation and Analysis of Path Planning Based on A* Algorithm

At present, path planning plays an important role in robot navigation, autonomous driving, and other fields. As a classical path search algorithm, the A* algorithm has been widely used in various path planning occasions. However, the algorithm has some problems in complex environments, such as low search efficiency and many path turns. In order to improve the search efficiency of the A algorithm in a complex environment and make its generated route smoother, this study improved the A* algorithm to enhance its application performance. Aiming at the problem of low search efficiency of the A* algorithm, the openlist node joining mechanism and neighborhood search getNeighbour method are adopted, which reduces unnecessary node expansion and significantly speeds up the search. At the same time, in order to limit the inflection point of the route and optimize the smoothness of the route, the estimated cost function is improved, and a smooth continuous path is generated by a spline curve, which reduces the corner frequency of robots or smart cars and improves the running stability and fluency. The simulation experiment in MATLAB shows that the improved A algorithm is superior to the traditional A algorithm in search efficiency, especially in large-scale complex maps. After path smoothing, the actual trajectory of the robot is more in line with the kinematics requirements, energy consumption is reduced, and collisions are effectively avoided in complex scenes such as narrow passages, which enhances the practicability and reliability of path planning. This study provides effective ideas and methods for optimising the A* algorithm in practical path planning applications and has positive significance for the development of related fields