Mobile robot path planning method based on improved A star strategy

Abstract

The invention relates to a mobile robot path planning method based on improved A star strategy. The method comprises the following steps: (a) processing laser data and mileage data acquired by a mobile robot by using a slam algorithm; (b) constructing a binaryzation raster map into a Voronoi map by utilizing a dynamic weinuo graph library; (c) constructing a corresponding Voronoi cost map according to the node coordinate on the Voronoi map; (d) transferring the starting position and the final position of the mobile robot to a map coordinate, and checking whether the starting and final coordinated indexes are legal or not; and (e) initializing g cost values of all nodes; and (f) smoothing the whole discrete path by using a smoother. The memory and calculation resources are greatly saved, the algorithmic convergence rate is accelerated; the Voronoi map is fused, so that the path is far away from barriers, and finally a best path is acquired by using the smoother.

Description

technical field [0001] The invention belongs to the field of industrial robots, and relates to a path planning method for a mobile robot, in particular to a path planning method for a mobile robot based on an improved A-star strategy. Background technique [0002] The main function of global path planning is to find an optimal or nearly optimal path from the starting point to the end point on the known static map. The path is generally continuous, smooth, free of obstacles, and suitable for the global path directly executed by the robot. . Path planning algorithm is the basis of the whole navigation system, and its results provide path information for subsequent local trajectory planning. [0003] There are many path planning algorithms commonly used at present, which can be mainly divided into three categories: methods based on graph search, such as Dijkstra, A* (namely A star), D*, Theta*, etc.; methods based on random adoption, such as random path Graph method (PRM), ra...

AI Technical Summary

Problems solved by technology

All heuristic-based algorithms need to model obstacles in a certain space, and usually have a slow convergence speed, which cannot meet the real-time requirements of robots.

The random sampling method avoids the modeling of the space, and finds a path with complete probability and is not optimal through random sampling points in the state space; but this type of method is not sensitive to the environment, and when there is a narrow channel, it often Convergence is very slow, and you may even be trapped in obstacles; in addition, safety has always been a prerequisite for industrial robots. The paths planned by existing algorithms are all close to obstacles, which cannot guarantee the safety of robots during walking

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