Robot path planning algorithm based on optimized ant colony algorithm

An ant colony algorithm and path planning technology, applied in the direction of navigation computing tools, etc., can solve the problems of falling into local optimum, poor adaptability, easy to fall into local optimum, etc., and achieve the effect of improving search efficiency and stability

Inactive Publication Date: 2018-04-17
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

For example, ImenChaari* combined the genetic algorithm with the ant colony algorithm. In the early stage, the genetic algorithm is used to generate the initial pheromone distribution, and the ant colony algorithm is used to find the optimal solution in the later stage, which can effectively combine the advantages of the two algorithms and improve the search efficiency of the ant colony. , but may fall into local optimum; X Wang et al. proposed a new path planning method based on particle swarm optimization (Particle Swarm Optimization, PSO) and ant colony optimization (Ant colony optimization, ACO) algorithm. Modular method, generate the path from the starting point to the goal point, and then distribute the pheromone based on the previously generated path, finally, use the improved...

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  • Robot path planning algorithm based on optimized ant colony algorithm
  • Robot path planning algorithm based on optimized ant colony algorithm
  • Robot path planning algorithm based on optimized ant colony algorithm

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Embodiment Construction

[0034] The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0035] The technical scheme that the present invention solves the problems of the technologies described above is:

[0036] Such as figure 1 As shown, the present invention provides a kind of mobile robot synchronous localization and map construction based on Gaussian distribution, and it comprises the following steps:

[0037] S1 inputs a matrix composed of 0 and 1 to represent the map that the robot needs to find the optimal path to establish the objective function;

[0038] S2 initializes each parameter. Including the unit length of the establishment of the grid; the basic parameters of the artificial potential field function; such as the gravitational coefficient; the number of ants searched by the ...

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Abstract

The invention relates to a robot path planning algorithm based on an optimized ant colony algorithm. The algorithm comprises the following steps: S1, initializing an inspiring factor to construct thecomprehensive inspiring information for the movement of the robot according to a target gravitation generated by an artificial potential field and an ant colony algorithm; S2, updating pheromone in the art colony algorithm by adopting a wolves distribution rule; and S3, optimizing a planned path by utilizing a path optimization algorithm. By adopting the robot path planning algorithm, an optimum path can be rapidly and efficiently planned.

Description

technical field [0001] The invention belongs to the field of mobile robot navigation, in particular to a robot path planning algorithm based on an optimal mixed ant colony algorithm. Background technique [0002] Path planning is one of the key technologies to realize the control of mobile robots. Its purpose is to find an optimal or suboptimal safe collision-free path from the starting position to the target position under certain environmental conditions and performance index requirements. For robot path planning, domestic and foreign scholars have proposed many planning methods, including artificial potential field method, neural network adaptive planning method, genetic algorithm, ant colony algorithm, particle swarm algorithm, etc. In recent years, more and more scholars pay more attention to the combination of multiple intelligent algorithms to improve the performance of the algorithm when studying the path planning problem. For example, ImenChaari* combined the gene...

Claims

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Application Information

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IPC IPC(8): G01C21/20
CPCG01C21/20
Inventor 张毅刘杰
Owner CHONGQING UNIV OF POSTS & TELECOMM
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