An Intelligent Robotic Grasping and Manipulation System with Sensor Fusion

Mingzhi Sha, Fan Zhu*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


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.

Original languageEnglish
Title of host publicationAdvances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023
EditorsAndrew Tan, Fan Zhu, Haochuan Jiang, Kazi Mostafa, Eng Hwa Yap, Leo Chen, Lillian J. A. Olule, Hyun Myung
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9789819984978
Publication statusPublished - 2024
EventInternational Conference on Intelligent Manufacturing and Robotics, ICIMR 2023 - Suzhou, China
Duration: 22 Aug 202323 Aug 2023

Publication series

NameLecture Notes in Networks and Systems
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ConferenceInternational Conference on Intelligent Manufacturing and Robotics, ICIMR 2023


  • Grasping pose strategy
  • Object recognition and pose estimation
  • Visual and tactile sensing fusion

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