Vehicle route planning method based on traversal multi-point regression

Abstract

The invention discloses a vehicle path planning method for traversing multiple points and returning to the origin, and relates to the technical field of computer intelligent path planning. The method comprises the steps of: prophetic condition; finding the minimum spanning tree; taking each subtree in the minimum spanning tree; seeking the most dense subtree; finding the most edge leaf 1 in the most dense subtree; Edge leaf endpoint 2; among the two most edge leaf endpoints, determine that one is the start point of the densest subtree, and the other is the end point of the densest subtree: use the root node of the densest subtree and the starting point of the densest subtree and the densest subtree The loop formed by the end point is the main line, and the leaf endpoints in the densest subtree are merged into the main line; the origin is merged with the main line to form a loop; the remaining subtrees are judged, and the steps above the subtrees with more than 1 points, let the subtrees Trees form loops; after all loops are merged, the final loop is our final path planning result. The method has the advantages of small calculation amount, fast calculation speed, strong stability and the like.

Description

technical field [0001] The present invention relates to the technical field of computer intelligent route planning, in particular to a vehicle route planning method based on traversal multi-point regression based on minimum spanning tree method. Background technique [0002] In the field of practical application, currently popular navigation software includes navigation apps such as Baidu, Gaode, and Tencent. Their main function is to realize a car to a destination without considering the return journey according to the needs of users and the actual traffic conditions. Reasonable path planning. In reality, more complex path planning is required, such as planning a reasonable plan for a truck to start from the logistics center, receive goods at multiple destinations, and return to the logistics center after receiving the goods. , and this kind of path planning is not available in the current navigation APP. [0003] Among the existing software service platforms, Didi Kuaich...

AI Technical Summary

Problems solved by technology

[0010] 2) Followed by ant colony algorithm, fish swarm algorithm, and annealing algorithm, such low-level IQ animal algorithms need not be compared with artificial intelligence algorithms

Secondly, these algorithms are local optimal algorithms, unstable, and the results may be different for each calculation, and once the path planning fails, it will be re-planned, and the time-consuming is definitely not low

[0011] 3) The genetic algorithm has the same disadvantages as the above algorithm. First of all, it is a local optimal algorithm without macro-control, and this algorithm is random (unstable), and it needs to be recalculated after failure, which takes a long time

[0012] In general, there is no efficient and stable near-optimal algorithm that is suitable for real-world applications

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