RGB-D-Based Robotic Grasping in Fusion Application Environments
Source abstract
Abstract
Although deep neural network (DNN)-based robot grasping has come a long way, the uncertainty of predicted results has prevented DNN-based approaches from meeting the stringent requirements of some industrial scenarios. To prevent these uncertainties from affecting the behavior of the robot, we break down the whole process into instance segmentation, clustering and planar extraction, which means we add some traditional approaches between the output of the instance segmentation network and the final control decision. We have experimented with challenging environments, and the results show that our approach can cope well with the challenging environment and achieve more stable and superior results than end-to-end grasping networks.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robot Manipulation and Learning · Soft Robotics and Applications · Fault Detection and Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Plasma Physics Chinese Academy of Sciences (ASIPP), Chinese Academy of Sciences, Hefei 230031, China
University of Science and Technology of China, Hefei 230026, China
Laboratory of Intelligent Machines, School of Energy Systems, LUT University, 53850 Lappeenranta, Finland
Institute of Energy, Hefei Comprehensive National Science Center, Hefei 230031, China
Funding
European Union · Comprehensive Research Facility for Fusion Technology Program of China · University Synergy Innovation Program of Anhui Province
References (20)
Show source-provided references
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