Path planning algorithm based on target direction constraint

Abstract

The invention discloses a path planning algorithm based on a target direction constraint. In a path searching process, a current node and expandable nodes in the same direction as a target node are only saved, the expandable nodes are added into a state space of the expandable nodes, each expandable node in the state space is evaluated, the obtained expandable node an evaluation function value of which is minimum is taken as a next current node, path searching is repeated until the expandable node an evaluation function value of which is minimum in the state space is the target node, and then an optimal path is obtained. In the path searching process, the current node and the expandable nodes in the same direction as the target node are only saved, the number of the nodes in the state space of the expandable nodes of the current node is reduced, searching scale of the algorithm is reduced, occupancy of a memory resource is reduced, searching efficiency of the algorithm is improved, and the path planning algorithm is suitable for path searching of various scenes having a high real-time requirement.

Description

technical field [0001] The invention belongs to the technical field of indoor guidance, and in particular relates to a path planning algorithm based on target direction constraints of indoor (such as traffic hubs, large shopping malls, etc.) path guidance technology. Background technique [0002] At present, there are many mature algorithms for the shortest path problem based on indoor guidance. Common path planning algorithms include Dijkstra algorithm, Floyd algorithm, and heuristic search algorithm. [0003] Dijkstra's algorithm is the most classic shortest path search algorithm, and it is also a relatively time-consuming algorithm. It finds the shortest path according to the increasing order of weight value, which is concise and clear, and the results obtained by algorithm search are also relatively accurate. On the other hand, for a sparse graph with a large number of input nodes, Dijkstra calculates the shortest path of the entire sparse graph whether it is to calcula...

AI Technical Summary

Problems solved by technology

In a more complex map, since all the expandable child nodes of the current node are retained when expanding the child nodes at each step, as the search progresses, more and more nodes need to be retained, and the number of retained nodes Too large, resulting in low search efficiency and large memory resources, so it is not suitable for some searches with high real-time requirements

Images