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A survey of path planning of industrial robots based on rapidly exploring random trees.
Luo, Sha; Zhang, Mingyue; Zhuang, Yongbo; Ma, Cheng; Li, Qingdang.
Afiliación
  • Luo S; College of Electromechanical Engineering, Qingdao University of Science and Technology, Shandong, China.
  • Zhang M; College of Electromechanical Engineering, Qingdao University of Science and Technology, Shandong, China.
  • Zhuang Y; College of Electromechanical Engineering, Qingdao University of Science and Technology, Shandong, China.
  • Ma C; College of Electromechanical Engineering, Qingdao University of Science and Technology, Shandong, China.
  • Li Q; College of Electromechanical Engineering, Qingdao University of Science and Technology, Shandong, China.
Front Neurorobot ; 17: 1268447, 2023.
Article en En | MEDLINE | ID: mdl-38023457
ABSTRACT
Path planning is an essential part of robot intelligence. In this paper, we summarize the characteristics of path planning of industrial robots. And owing to the probabilistic completeness, we review the rapidly-exploring random tree (RRT) algorithm which is widely used in the path planning of industrial robots. Aiming at the shortcomings of the RRT algorithm, this paper investigates the RRT algorithm for path planning of industrial robots in order to improve its intelligence. Finally, the future development direction of the RRT algorithm for path planning of industrial robots is proposed. The study results have particularly guided significance for the development of the path planning of industrial robots and the applicability and practicability of the RRT algorithm.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Neurorobot Año: 2023 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Neurorobot Año: 2023 Tipo del documento: Article País de afiliación: China
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