Path Planning Algorithm Based on Target Direction Constraint

Abstract

The invention discloses a path planning algorithm based on target direction constraints. In the path search process, only the expandable nodes in the same direction as the current node and the target node are reserved, and these expandable nodes are added to the state space of the expandable nodes. , evaluate each scalable node in the state space, and get the scalable node with the smallest evaluation function value as the next current node, repeat the path search until the scalable node with the smallest evaluation function value in the state space is the target node, and get optimal path. In the path search process, the present invention only retains the expandable nodes in the same direction as the current node and the target node, reduces the number of nodes in the state space of the expanded nodes of the current node, reduces the search scale of the algorithm, and reduces memory resources Occupancy improves the search efficiency of the algorithm and is suitable for path search in various scenarios with high real-time requirements.

Description

Technical field [0001] The invention belongs to the technical field of indoor guidance, and specifically relates to a path planning algorithm based on target direction constraints of indoor (such as transportation 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 algorithm is the most classic shortest path search algorithm, and it is also a relatively time-consuming algorithm. It finds the shortest path in the order of increasing weight, which is concise and clear, and the results obtained by the algorithm search are also relatively accurate. On the other hand, for sparse graphs with a large number of input nodes, Dijkstra calculates the shortest path of the entire sparse graph, whether it is calculating two specified...

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