Path Planning of Follow-up Transport Robot Based on Machine Vision
Proceedings - 2022 2nd International Symposium on Artificial Intelligence and its Application on Media, ISAIAM 2022
; : 43-47, 2022.
Article
Dans Anglais
| Scopus | ID: covidwho-20243436
ABSTRACT
With the upgrading and innovation of the logistics industry, the requirements for the level of transportation smart technologies continue to increase. The outbreak of the COVID-19 has further promoted the development of unmanned transportation machines. Aimed at the requirements of intelligent following and automatic obstacle avoidance of mobile robots in dynamic and complex environments, this paper uses machine vision to realize the visual perception function, and studies the real-time path planning of robots in complicated environment. And this paper proposes the Dijkstra-ant colony optimization (ACO) fusion algorithm, the environment model is established by the link viewable method, the Dijkstra algorithm plans the initial path. The introduction of immune operators improves the ant colony algorithm to optimize the initial path. Finally, the simulation experiment proves that the fusion algorithm has good reliability in a dynamic environment. © 2022 IEEE.
ant colony optimization; follow-up transport robot; machine vision; path planning; Intelligent robots; Intelligent vehicle highway systems; Motion planning; Robot programming; Complex environments; Dynamic environments; Follow up; Fusion algorithms; Logistics industry; Machine-vision; Obstacles avoidance; On-machines; Smart technology; Computer vision
Texte intégral:
Disponible
Collection:
Bases de données des oragnisations internationales
Base de données:
Scopus
Type d'étude:
Étude de cohorte
/
Étude pronostique
langue:
Anglais
Revue:
Proceedings - 2022 2nd International Symposium on Artificial Intelligence and its Application on Media, ISAIAM 2022
Année:
2022
Type de document:
Article
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