Route planning method and system based on genetic ant colony algorithm

A technology of path planning and ant colony algorithm, applied in genetic rules, genetic models, two-dimensional position/channel control, etc., can solve problems such as blindness of ant colony algorithm

Inactive Publication Date: 2016-04-27
JIANGSU UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a path planning method and system

Method used

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  • Route planning method and system based on genetic ant colony algorithm
  • Route planning method and system based on genetic ant colony algorithm
  • Route planning method and system based on genetic ant colony algorithm

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Experimental program
Comparison scheme
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Embodiment 1

[0067] Such as figure 1 and figure 2 As shown, Embodiment 1 provides a path planning method, including the following steps:

[0068] Step S1, converting a part of the optimized solution obtained by the genetic algorithm into the initial value of the pheromone of the ant colony algorithm;

[0069] In step S2, the path optimization is carried out through the ant colony algorithm, and after the optimization is completed, the cross operation is performed on the qualified paths, and finally the optimal path is obtained.

[0070] As the initial value of pheromone for ant colony algorithm An optional implementation of .

[0071] In the step S2, the method of converting a part of the optimized solution obtained by the genetic algorithm into the initial value of the pheromone of the ant colony algorithm includes:

[0072] Step S11, modeling the environment;

[0073] Specifically, the environment modeling, that is, the starting point of a certain object is the coordinate origin, ...

Embodiment 2

[0109] Such as figure 1 and figure 2 As shown, on the basis of embodiment 1, this embodiment 2 provides a path planning system, including:

[0110] A pheromone obtaining module for obtaining an initial value of the pheromone of the ant colony algorithm, and

[0111] An optimal path acquisition module connected with the pheromone acquisition module.

[0112] The pheromone acquisition module is suitable for converting a part of the optimized solution obtained by the genetic algorithm into the initial value of the pheromone of the ant colony algorithm; that is, modeling the environment, initializing the genetic parameters to generate the initial population; setting the fitness function, and calculating each the fitness of the population; and convert the top 50% group solutions with higher fitness into the initial value of pheromone for the ant colony algorithm Get initial value of pheromone For the specific methods involved, please refer to the related discussion of Exampl...

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Abstract

The invention relates to a route planning method and system based on a genetic ant colony algorithm. The route planning method comprises the following steps: S1, converting a part of optimal solution obtained by a genetic algorithm into a pheromone initial value of an ant colony algorithm; and S2, performing route optimization after the ant colony algorithm, and then performing crossover operation on the paths satisfying the condition to obtain an optimal path. The route planning method and system overcome inevitable defects in the single ant colony algorithm, namely high blindness of the ant colony algorithm at the initial search phase, realize advantage complement of the ant colony and genetic algorithms, reduce the path search range and improve the search efficiency of the optimal path.

Description

technical field [0001] The invention relates to the technical field of robot intelligent algorithms, in particular to a path planning method based on a genetic ant colony algorithm. Background technique [0002] Mobile robots are an important field of intelligent control technology. In addition to being used in space exploration, ocean development, and atomic energy, they also have broad application prospects in factory automation, construction, mining, risk elimination, military, service, and agriculture. There are many methods of path planning, such as the steepest descent method, artificial potential field method, fuzzy reasoning method, etc. Using the steepest descent method converges slowly, is not efficient, and sometimes does not reach the optimal solution; using the artificial potential field method is convenient for real-time control, but lack of global information, there is a problem of local optimal value; the biggest advantage of using fuzzy inference method is t...

Claims

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Application Information

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IPC IPC(8): G05D1/02G06N3/12
CPCG05D1/02G06N3/126
Inventor 贺乃宝陆毅高倩沈琳罗印升潘瑜刘波俞烨
Owner JIANGSU UNIV OF TECH
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