Strip-shaped robot path planning method based on self-learning ant colony algorithm

A technology of path planning and ant colony algorithm, which is applied in the direction of instruments, calculations, navigation calculation tools, etc., can solve problems such as algorithm efficiency and stability need to be improved, grid density is low, and the number of feasible cells is reduced

Active Publication Date: 2018-08-17
HUAIAN COLLEGE OF INFORMATION TECH
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Problems solved by technology

Since the expanded cells are regarded as obstacle cells, although this approach can effectively avoid collisions, it will also result in low grid density or a significant reduction in the number of feasible cells, which directly affects the calculation effect of the algorithm
In addition, in the robot path planning method based on the ant colony algorithm, the ant colony algorithm has less self-learning ability, so the efficiency and stability of the algorithm need to be improved

Method used

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  • Strip-shaped robot path planning method based on self-learning ant colony algorithm
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  • Strip-shaped robot path planning method based on self-learning ant colony algorithm

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[0047] The technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings and embodiments, but it should not be construed as a limitation on the technical solution. In the following description, numerous specific details are given in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced without one or more of these details. In other examples, some technical features known in the art are not described in order to avoid confusion with the present invention.

[0048] figure 1 The overall flow of the robot path planning method of the present invention is given, please refer to figure 1 , the following is a detailed description of each step in the method.

[0049] Step S101: Model the workspace, generate an m×n grid map, and implement computer storage; the cells in the grid map are marked as B θ (x ,...

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Abstract

The invention discloses a strip-shaped robot path planning method based on a self-learning ant colony algorithm. The strip-shaped robot path planning method based on the self-learning ant colony algorithm is characterized by comprising the following steps: step 1, environment modeling; step 2, initializing stage; step 3, initial searching; step 4, overall updating of grid map pheromone; step 5, self-learning searching; and step 6, outputting of a planning path. The strip-shaped robot path planning method based on the self-learning ant colony algorithm is greatly improved for an ant colony algorithm calculating process, a self-learning strategy is introduced, grid-process environment modeling is treated specially, by the used grid method, the ant colony algorithm deals with a strip-shaped robot path planning process under the condition that barrier cells do not need to be expanded, a new shortest path calculating method is provided, thought of machine learning is fused in the ant colonyalgorithm, the efficiency of path planning of the ant colony algorithm is improved by effective combination of methods such as pheromone, heuristic information, positive feedback and greedy search, and a strip-shaped robot can pass through a narrow channel according to the outline of the strip-shaped robot so as to implement the shortest path planning.

Description

technical field [0001] The invention relates to a path planning method for a strip robot based on a self-learning ant colony algorithm. Background technique [0002] Ant colony algorithm is a bionic algorithm based on swarm intelligence, which is widely used in scientific research and engineering fields. The basis of the ant colony algorithm is probability calculation. This type of calculation method cannot guarantee that the optimal solution will be obtained for each calculation, but it can obtain a relatively optimal problem solution with high efficiency. In the field of engineering with low precision requirements, relatively optimal problem solutions are acceptable in most cases, which is one of the main reasons why the ant colony algorithm has been widely studied and applied. [0003] Robot path planning is one of the main research directions in the field of robotics. In the existing robot path planning methods based on the grid method environment modeling, the mobile ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/20G05D1/02G06Q10/04
CPCG01C21/20G05D1/0221G06Q10/047
Inventor 程乐杨晔华大龙宋艳红姜仲秋刘万辉潘永安郜继红
Owner HUAIAN COLLEGE OF INFORMATION TECH
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