Search method for path planning

Abstract

The invention discloses a search method for path planning, comprising the following steps: each individual in a population of a path planning scheme corresponding to a path diagram is initialized, wherein each individual S comprises a task loop sequence for planning each vehicle according to the path diagram, and each task loop comprises a task loop sequence corresponding to a vehicle; the local search with a traditional step length and the local search with a variable step length greater than the traditional step length are sequentially executed on each individual S to obtain a correspondinglocal optimal solution individual S'; all the local optimal solution individuals S' are ordered according to the total consumption of all the loops corresponding to each individual and / or the degree of violating capacity constraint of each individual; and the optimal path planning scheme of the path diagram is determined according to the sequence. The invention has favorable path planning performance.

Description

technical field [0001] The invention belongs to the field of transportation intelligence, in particular to a random search algorithm for path planning. Background technique [0002] The path planning problem is a classic combinatorial optimization problem, which has a wide range of applications in the industrial field. For example, problems such as sprinkler routing, garbage collection, letter delivery, and school bus scheduling in cities are all regarded as path planning problems. The path planning problem can be regarded as constructing several circuits on a given graph, so that these circuits satisfy some specific conditions and constraints and the total cost of routing these circuits is the least. Since route planning problems often involve huge markets or huge amounts of money, it is very necessary to design effective solutions. However, it is proved by theory that the path planning problem is a NP (non-deterministic polynomial time) difficult problem, that is, the ti...

AI Technical Summary

Problems solved by technology

However, it is proved by theory that the path planning problem is a NP (non-deterministic polynomial time) difficult problem, that is, the time to find the global optimal solution of the problem increases exponentially with the increase of the problem size

However, these methods have a common defect, that is, they all use the traditional small-step local search, and at each step of the local search, only a solution that is very similar to the current solution can be generated.

In some cases, for example, when the solution space of the problem is large or the capacity constraints are strict, the solution space is composed of a large number of scattered feasible regions, and the traditional small-step local search will not be able to achieve the ideal result.

In the former case, it may take many steps of local search to reach the global optimal solution from the current solution, while in the latter case, the traditional small-step local search may cause the search to fail to cross the non-feasible interval between feasible regions. area so as to jump out of the current local optimal solution

[0004] Therefore, the determination algorithm can only be applied to small-scale path planning problems, but cannot be applied to medium or large-scale problems that often appear in practice.

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