Self-Recognition Grasping Operation with a Vision-Based Redundant Manipulator System

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.