標題: | Vision-Based Adaptive Grasping of a Humanoid Robot Arm |
作者: | Song, Kai-Tai Tsai, Shih-Cheng 電子工程學系及電子研究所 Department of Electronics Engineering and Institute of Electronics |
關鍵字: | Grasping control;safe operation;vision-based grasping;visual servo;Kinect Sensor |
公開日期: | 2012 |
摘要: | 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. |
URI: | http://hdl.handle.net/11536/20745 |
ISBN: | 978-1-4673-0364-4 |
ISSN: | 2161-8151 |
期刊: | 2012 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS (ICAL) |
起始頁: | 155 |
結束頁: | 160 |
Appears in Collections: | Conferences Paper |