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Welding path planning method and system based on hybrid teaching and learning algorithm

A welding path and optimization algorithm technology, applied in manufacturing computing systems, welding equipment, auxiliary welding equipment, etc., can solve problems such as slow search speed, easy "prematurity, and low accuracy of solutions"

Inactive Publication Date: 2020-07-03
SOUTH CENTRAL UNIVERSITY FOR NATIONALITIES
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Problems solved by technology

[0005] Aiming at the above defects or improvement needs of the prior art, the present invention provides a welding path planning method based on a hybrid teaching and learning algorithm. ”, and the technical problems of falling into local optimum, as well as the technical problems of poor validity and low solution accuracy of the existing welding path planning method due to limited search ability, and the existing welding path planning method is not fast enough due to the search speed. Technical issues with slow convergence and low efficiency

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  • Welding path planning method and system based on hybrid teaching and learning algorithm
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[0054] In order to make the objectives, technical solutions and advantages of the present invention clearer, the following further describes the present invention in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0055] The basic idea of ​​the present invention is to provide a welding path planning method based on a hybrid teaching and learning algorithm to improve the welding efficiency of the robot. The method first improves the teaching and learning algorithm, uses the genetic algorithm discretization method to discretize the traditional teaching and learning algorithm, and then expands and optimizes the ori...

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Abstract

The invention discloses a welding path planning method based on a hybrid teaching and learning algorithm, and aims to improve the welding efficiency of a robot. The method comprises the following steps of: firstly, improving a teaching and learning algorithm, discretizing a traditional teaching and learning algorithm by adopting a genetic algorithm discretization method, and then expanding and optimizing the original two-stage teaching process into five stages of teacher training, teacher teaching, student learning, student self-learning and teacher reverse learning; and secondly, mixing the discretized teaching and learning optimization algorithm with a plurality of intelligent optimization algorithms, and combining the concentricity of teaching and learning optimization algorithm operators and the diversity of simulated annealing algorithm operators, so that the diversity of solutions of the hybrid teaching and learning algorithm is enhanced, the local search and global search capability of the algorithm is effectively balanced, the algorithm convergence rate is increased, and the algorithm can search an optimal path more quickly.

Description

Technical field [0001] The invention belongs to the technical field of welding machine robot control, and more specifically, is a welding path planning method and system based on a hybrid discrete teaching and learning optimization algorithm. Background technique [0002] Welding robots are widely used in the field of automated manufacturing, and reasonable welding path planning helps greatly improve welding efficiency. Therefore, the welding path planning of welding robots has become an important optimization problem in the manufacturing field, and seeking an efficient method to solve this problem has high application value. [0003] The existing welding path planning methods for welding robots are mainly the welding path planning method based on Particle Swarm Optimization (PSO) and the welding path planning method based on genetic algorithm (namely Genetic Algorithm). The former is to train the collision penalty function by neural network to obtain the collision-free path, and ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B23K37/02B25J9/16G06N3/00G06Q10/04G06Q50/04
CPCB23K37/0252B25J9/1664G06N3/006G06Q10/047G06Q50/04Y02P90/30
Inventor 何湘竹高怡杰石英李成华
Owner SOUTH CENTRAL UNIVERSITY FOR NATIONALITIES