TY - GEN
T1 - An Intelligent Robotic Grasping and Manipulation System with Sensor Fusion
AU - Sha, Mingzhi
AU - Zhu, Fan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Complex robotic grasping tasks, for example, deformable objects grasping and adaptive grasping, which are close to practical robotic application, remain a challenge due to unknown object geometries. In this paper, an intelligent robotic grasping and manipulation system with sensor fusion that consists of visual-based object detection and 6-D pose estimation, grasping pose estimation, robot control, and safe grasping force framework based on deep learning is proposed. It is designed to guarantee safe grasping by estimating appropriate grasping force. The results show that our system has a good performance on the detection and grasping pose adjustment of trained objects with different texture. The safe grasping force estimation has a better performance on rough texture and limited deformable objects.
AB - Complex robotic grasping tasks, for example, deformable objects grasping and adaptive grasping, which are close to practical robotic application, remain a challenge due to unknown object geometries. In this paper, an intelligent robotic grasping and manipulation system with sensor fusion that consists of visual-based object detection and 6-D pose estimation, grasping pose estimation, robot control, and safe grasping force framework based on deep learning is proposed. It is designed to guarantee safe grasping by estimating appropriate grasping force. The results show that our system has a good performance on the detection and grasping pose adjustment of trained objects with different texture. The safe grasping force estimation has a better performance on rough texture and limited deformable objects.
KW - Grasping pose strategy
KW - Object recognition and pose estimation
KW - Visual and tactile sensing fusion
UR - http://www.scopus.com/inward/record.url?scp=85187781637&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-8498-5_19
DO - 10.1007/978-981-99-8498-5_19
M3 - Conference Proceeding
AN - SCOPUS:85187781637
SN - 9789819984978
T3 - Lecture Notes in Networks and Systems
SP - 251
EP - 263
BT - Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023
A2 - Tan, Andrew
A2 - Zhu, Fan
A2 - Jiang, Haochuan
A2 - Mostafa, Kazi
A2 - Yap, Eng Hwa
A2 - Chen, Leo
A2 - Olule, Lillian J. A.
A2 - Myung, Hyun
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023
Y2 - 22 August 2023 through 23 August 2023
ER -