Global dynamic smooth path planning method based on optimized A-STAR algorithm

Abstract

The invention discloses a global dynamic smooth path planning method based on an optimized A-STAR algorithm, and the method comprises the steps: carrying out the rectangular processing of an obstacle at an earlier stage, finding a path in a barrier-free region through employing a conventional A * algorithm, employing the optimized A * algorithm when the obstacle is encountered, adding the path to a path list for calculation according to a selected path point after the path planning is completed for the first time, and carrying out the calculation of the path point. And then path smoothing processing is carried out by using an artificial potential field method. According to the method, not only can the searched path be ensured to have smoothness and real-time obstacle avoidance capability, but also the path-finding efficiency is improved on the basis of ensuring global optimum.

Description

technical field [0001] The invention relates to the technical field of dynamic path planning, in particular to a global dynamic smooth path planning method based on an optimized A-STAR algorithm. Background technique [0002] The A* algorithm is a classic heuristic algorithm, which can efficiently find the optimal path from the starting point to the target point in a static environment. This algorithm is simple in program and easy to implement. At present, during the operation of the traditional A* algorithm, two lists, the Open list and the Close list, need to be maintained. A* needs to calculate the surrounding nodes of the current position during the pathfinding process. When encountering an obstacle, it will re-find the path near the starting point, and repeat until the search width exceeds the obstacle width, and then continue to Therefore, there will be a large number of useless nodes that need to be calculated, resulting in excessive computation, excessive memory usa...

AI Technical Summary

Problems solved by technology

A* needs to calculate the surrounding nodes of the current position during the pathfinding process. When encountering an obstacle, it will re-find the path near the starting point, and repeat until the search width exceeds the obstacle width, and then continue to Therefore, there will be a large number of useless nodes that need to be calculated, resulting in excessive computation, excessive memory usage, and long pathfinding time.

And there are unnecessary turning points in the planned path. The traditional A* algorithm does not smooth the path, so the planned path turns sharply, which is not conducive to the robot's path tracking.

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