Vision-based adaptive grasping of a humanoid robot arm

Kai-Tai Song*, Shih Cheng Tsai

*Corresponding author for this work

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

12 Scopus citations

Abstract

This paper presents a motion planning and control design of a humanoid robot arm for vision-based grasping in an obstructed environment. A Kinect depth camera is utilized to recognize and find the target object in the environment and grasp it in real-time. First, gradient direction in a depth image is applied to segment environment into several planes. Then, speed up robust feature(SURF) is used to match features between segmented planes and locate the target object. This approach effectively speeds up the matching operation by decreasing the area to match in image planes. Moreover, this study proposes a design for safe operation of the robot arm in an unknown environment. Two safe indices are designed to improve the robustness in safe grasping in an obstructed environment. One index defines the degree of influence of obstacles to the manipulator. Another index classifies the workspace into three regions, namely safe, uncertainty and danger region. The robot employs these indices to move to safe regions by using a potential field for motion planning. Practical experiments show that the six degree-of-freedom robot arm can effectively avoid obstacles and complete the grasping task.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Automation and Logistics, ICAL 2012
Pages155-160
Number of pages6
DOIs
StatePublished - 1 Nov 2012
Event2012 IEEE International Conference on Automation and Logistics, ICAL 2012 - Zhengzhou, China
Duration: 15 Aug 201217 Aug 2012

Publication series

NameIEEE International Conference on Automation and Logistics, ICAL
ISSN (Print)2161-8151

Conference

Conference2012 IEEE International Conference on Automation and Logistics, ICAL 2012
CountryChina
CityZhengzhou
Period15/08/1217/08/12

Keywords

  • Grasping control
  • Kinect Sensor
  • safe operation
  • vision-based grasping
  • visual servo

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