Path Planning Method for Bar Robot Based on Self-learning Ant Colony Algorithm

A technology of path planning and ant colony algorithm, which is applied in the direction of instruments, computing, navigation computing tools, etc., can solve the problems that the efficiency and stability of the algorithm need to be improved, affect the calculation effect of the algorithm, and the ant colony algorithm has little self-learning ability, etc.

Active Publication Date: 2021-06-08
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

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  • Path Planning Method for Bar Robot Based on Self-learning Ant Colony Algorithm
  • Path Planning Method for Bar Robot Based on Self-learning Ant Colony Algorithm
  • Path Planning Method for Bar Robot 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 path planning method for a strip robot based on a self-learning ant colony algorithm, which is characterized in that the path planning method for a strip robot comprises the following steps: step 1 environment modeling; step 2 initialization stage; step 3 initial search; step 4 Globally update the raster map pheromone; step 5 self-learning search; step 6 output planning path. The present invention has greatly improved the calculation process of the ant colony algorithm, introduced a self-learning strategy, and made special treatment for the grid method environment modeling. To deal with the path planning problem of strip robots, provide a new shortest path calculation method, integrate the idea of ​​machine learning in the ant colony algorithm, and effectively combine pheromone, heuristic information, positive feedback, greedy search and other methods to improve The efficiency of the path planning of the ant colony algorithm, the bar robot can complete the crossing of the narrow passage according to its own shape, so as to realize 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 ...

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Application Information

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Patent Type & Authority Patents(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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