Obstacle Avoidance Path Planning for a 6-DOF Manipulator Based on Improved RRT Algorithm

Zhuo, Zhang; Haiyan, He; Miaomiao, Zhai

doi:10.1109/icnc-fskd59587.2023.10280813

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…A hyper-redundant manipulator (HRM) has an extremely high degree of freedom, and an ellipsoid-shape rapidly-expanding random tree (E-RRT*) constrains the unwanted angles of the HRM and enhances the path planning [82]. Zhang Zhuo combines the APF method and proposes an improved RRT algorithm for path planning to achieve trajectory optimization and to ensure the safe, smooth, and fast movements of the robotic arm [83]. The improved RRT algorithm successfully enables the robotic arm to achieve obstacle avoidance.…”

Section: Motion Controlmentioning

confidence: 99%

Bibliometric analysis and visualization of research and development trends in humanoid robotics

Zhang,

Wang,

Zhou

et al. 2024

AETR

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This study searched the Web of Science core database for literature related to robotic arms for humanoid robots and used bibliometrics and CiteSpace 6.1.R2 software to visually analyze the authors, institutions, and keywords of the literature published in this field from 2009 to 2023. The study summarizes the current status and trends of robotic arms for humanoid robots and incorporates information extraction to select and include 376 articles for analysis. This study provides the field's development vein by reviewing the relevant literature and follows the research progress in the area of humanoid robots in a particular vein. In addition to thoroughly examining robotic arms and motion generating techniques for robotic arms that mimic human action, this study reviews pertinent literature. A review of the literature indicates a clear trend toward the use of artificial intelligence and machine learning to create motion control schemes for humanoid robotic arms. The report then assesses current methodologies and suggests future research directions for promising studies.

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Section: Motion Controlmentioning

confidence: 99%