Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Chaoquan Shi"'
Publikováno v:
Machines, Vol 12, Iss 8, p 506 (2024)
Objects in cluttered environments may have similar sizes and shapes, which remains a huge challenge for robot grasping manipulation. The existing segmentation methods, such as Mask R-CNN and Yolo-v8, tend to lose the shape details of objects when dea
Externí odkaz:
https://doaj.org/article/53732cb5218747e5b645c0696d05e762
Publikováno v:
Sensors, Vol 22, Iss 11, p 4283 (2022)
Robotics grasp detection has mostly used the extraction of candidate grasping rectangles; those discrete sampling methods are time-consuming and may ignore the potential best grasp synthesis. This paper proposes a new pixel-level grasping detection m
Externí odkaz:
https://doaj.org/article/e26e0e651bad4258b9527b3bca3941ed
Publikováno v:
2022 IEEE 5th International Conference on Electronics Technology (ICET).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d96dc658774ae19ed3b704f7654981d3
https://doi.org/10.1109/icet55676.2022.9825184
https://doi.org/10.1109/icet55676.2022.9825184
Publikováno v:
Intelligent Robotics and Applications ISBN: 9783030890971
ICIRA (2)
ICIRA (2)
Current visual servoing methods used in robot manipulation require system modeling and parameters, only working in structured environments. This paper presents a self-learning visual servoing for a robot manipulator operated in unstructured environme
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aa46d1854c11985c0a159f18e58ad39b
https://doi.org/10.1007/978-3-030-89098-8_5
https://doi.org/10.1007/978-3-030-89098-8_5
