A fast and global search method for grasping pose optimization in manufacturing
| Autor: | Shu Liu, Jingjia Shi, Jun Zhan, Detian Zeng |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
0209 industrial biotechnology 020901 industrial engineering & automation Artificial Intelligence Computer science 0202 electrical engineering electronic engineering information engineering General Engineering 020201 artificial intelligence & image processing 02 engineering and technology |
| Zdroj: | Journal of Intelligent & Fuzzy Systems. 41:1713-1726 |
| ISSN: | 1875-8967 1064-1246 |
| Popis: | To use the electromagnetic chuck to precisely absorb industrial parts in manufacturing, this paper presents a hybrid algorithm for grasping pose optimization, especially for the part with a large surface area and irregular shape. The hybrid algorithm is based on the Gaussian distribution sampling and the hybrid particle swarm optimization (PSO). The Gaussian distribution sampling based on the geometric center point is used to initialize the population, and the dynamic Alpha-stable mutation enhances the global optimization capability of the hybrid algorithm. Compared with other algorithms, the experimental results show that ours achieves the best results on the dataset presented in this work. Moreover, the time cost of the hybrid algorithm is near a fifth of the conventional PSO in the discovery of optimal grasping pose. In summary, the proposed algorithm satisfies the real-time requirements in industrial production and still has the highest success rate, which has been deployed on the actual production line of SANY Group. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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