Intelligent transport vehicle path planning method

Abstract

The invention discloses an intelligent transport vehicle path planning method, which comprises the steps of simplifying a four-axis AGV model to obtain a kinematic model of an AGV; treating the map ofan engineering scene into a two-dimensional grid map; simplifying the transportation process of the AGV from the aspects of boundary dimension, acceleration and internal interaction; establishing a cost function and a weight coefficient of an A* algorithm; introducing an obstacle avoidance coefficient delta to meet obstacle avoidance requirements, generating a path, and carrying out queue optimization, hierarchical routing and obstacle avoidance judgment in the generation process; optimizing the A* algorithm through an improved Floyd algorithm; and optimizing the A* algorithm on the basis ofconsidering the influence of the kinematic model of the AGV on the algorithm, and selecting a line with the shortest time consumption. According to the method, an environmental modeling technology andan A* improved algorithm are matched with each other, a kinematic model of a vehicle is considered, and the model is substituted into the algorithm for optimization; and through mutual advantage complementation, the efficiency and the safety level of the algorithm are effectively improved.

Description

Technical field[0001]The present invention belongs to the field of automotive path planning, and more particularly to an intelligent transport vehicle path planning method.Background technique[0002]Intelligent transport vehicle path planning technology has become an important technique in unmanned applications in the region, its automation is high, high efficiency, and low cost, and low cost began to be used in the field of intelligent container terminals, intelligent plants. The path planning can be divided into two parts of the path and plan, where the sequence point or curve connected to the starting point position and the end point position is called the path, the policy is called the path. The main task of the path planning method refers to the current vehicle information, reasonably explore the environment space, and finally plans a path that facilitates the vehicle execution, no collision. The existing path planning method has analog implantation algorithm, artificial field m...

AI Technical Summary

Problems solved by technology

[0006] There are more or less problems in the above algorithms. The main disadvantages of the existing algorithms are: the optimal path obtained by running the same algorithm at the same time is not necessarily the same, and the path is generally close to the edge of the obstacle. Considering the problem of safe interaction Insufficient; the time-consuming problem of the algorithm has not been effectively solved, the degree of optimization of the algorithm is not enough, and the above algorithm is not comprehensive enough to consider the actual AGV environment modeling

[0007] In the graph search algorithm based on the grid method, the heuristic search A* algorithm is simple in principle, easy to implement and can meet the requirements, and has been widely used in engineering operations. However, the traditional A* algorithm has long operation time and cannot be guaranteed to be the best The problem with optimal search paths

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