Zobrazeno 1 - 10
of 858
pro vyhledávání: '"Robotic Grasping"'
Publikováno v:
Alexandria Engineering Journal, Vol 102, Iss , Pp 149-158 (2024)
This research presents a novel approach to robotic grasping by integrating an attention mechanism and advanced U-Net architectures, specifically UNet and UNet++, into the Generative Grasping Convolutional Neural Network (GG-CNN). The proposed method,
Externí odkaz:
https://doaj.org/article/8f5a8d7f65624d4787a3d907a6cce204
Autor:
Werner Friedl
Publikováno v:
Frontiers in Robotics and AI, Vol 11 (2024)
For certain tasks in logistics, especially bin picking and packing, humans resort to a strategy of grasping multiple objects simultaneously, thus reducing picking and transport time. In contrast, robotic systems mainly grasp only one object per picki
Externí odkaz:
https://doaj.org/article/612a6a64f7414277977902397bd601b2
Publikováno v:
In Robotics and Computer-Integrated Manufacturing December 2024 90
Publikováno v:
Mathematical Biosciences and Engineering, Vol 21, Iss 2, Pp 3448-3472 (2024)
Dexterous grasping is essential for the fine manipulation tasks of intelligent robots; however, its application in stacking scenarios remains a challenge. In this study, we aimed to propose a two-phase approach for grasp detection of sequential robot
Externí odkaz:
https://doaj.org/article/cf2d88d0f51d4a6c9894af757f85df1a
Publikováno v:
Machines, Vol 12, Iss 11, p 786 (2024)
To meet real-time grasping demands in complex environments, this paper proposes a lightweight yet high-performance robotic grasping model. The model integrates large kernel convolution and residual connections to generate grasping information for unk
Externí odkaz:
https://doaj.org/article/98b472b99229460f96ca32e69224327d
Publikováno v:
In Alexandria Engineering Journal September 2024 102:149-158
Autor:
Kuldeep R. Barad, Andrej Orsula, Antoine Richard, Jan Dentler, Miguel A. Olivares-Mendez, Carol Martinez
Publikováno v:
IEEE Access, Vol 12, Pp 164621-164633 (2024)
Vision-based grasping of unknown objects in unstructured environments is a key challenge for autonomous robotic manipulation. A practical grasp synthesis system is required to generate a diverse set of 6-DoF grasps from which a task-relevant grasp ca
Externí odkaz:
https://doaj.org/article/3aa0061e2f56463fb1f8a431b27521b9
Publikováno v:
IEEE Access, Vol 12, Pp 138047-138060 (2024)
Precise pose estimation of textureless objects from RGB images without the use of depth information remains a significant challenge in computer vision. This paper introduces an RGB-based method for 6D pose estimation, designed for robotic grasping in
Externí odkaz:
https://doaj.org/article/3bc1c79454ab45039a7a588d417e1197
Publikováno v:
In Expert Systems With Applications 1 June 2024 243