Self-Recognition Grasping Operation with a Vision-Based Redundant Manipulator System
Source abstract
Abstract
For unstructured environment applications, the ability of self-recognition for grasping operations should be guaranteed for manipulators. For this purpose, a grasping process, including instance segmentation, pose estimation, and pose transformation, is proposed herein to achieve autonomous object detection, location detection, and grasp planning. An inverse solution in position form is derived for pose transformation to guarantee redundant manipulator adaption. The inverse solution requires no default initial configuration and can obtain all feasible solutions for grasping. Additionally, the optimal grasp can be selected by introducing an optimal factor, such as manipulability. Besides, the process is programmed with high computational efficiency, making it a better choice for manipulators to achieve self-recognized grasping operation. Experiments are carried out herein to verify the necessity of instance segmentation, pose estimation, and pose transformation in achieving self-recognized grasping operation. The inverse solution in the position form is also proven to be efficient and adaptable for the pose transformation of redundant manipulators.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robot Manipulation and Learning · Robotics and Sensor-Based Localization · Hand Gesture Recognition Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Transducer Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Beijing Municipality · Key Research Program of Frontier Sciences, CAS
References (38)
Show source-provided references
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