Authors:
Kamil Khusnutdinov
1
;
Artur Sagitov
1
;
Ayrat Yakupov
1
;
Roman Meshcheryakov
2
;
Kuo-Hsien Hsia
3
;
Edgar A. Martinez-Garcia
4
and
Evgeni Magid
1
Affiliations:
1
Department of Intelligent Robotics, Higher Institute for Information Technology and Intelligent Systems, Kazan Federal University, 35 Kremlyovskaya street, Kazan and Russian Federation
;
2
V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, 65 Profsoyuznaya street, Moscow, 117997 and Russian Federation
;
3
Department of Electrical Engineering, Far East University, Zhonghua Road 49, Xinshi District, Tainan City and Taiwan
;
4
Universidad Autonoma de Ciudad Juarez, Cd. Juarez Chihuahua, 32310 and Mexico
Keyword(s):
Algorithm, Grasp Planning, Hand Pose Detection, Humanoid Robot.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Formal Methods
;
Humanoid Robots
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Planning and Scheduling
;
Robotics and Automation
;
Simulation and Modeling
;
Symbolic Systems
Abstract:
In robot manipulator control, grasping different types of objects is an important task, but despite being a subject of many studies, there is still no universal approach. A humanoid robot arm end-effector has a significantly more complicated structure than the one of an industrial manipulator. It complicates a process of object grasping, but could possibly make it more robust and stable. A success of grasping strongly depends on a method of determining an object shape and a manipulator grasping procedure. Combining these factors turns object grasping by a humanoid into an interesting and versatile control problem. This paper presents a grasping algorithm for AR-601M humanoid arm with mimic joints in the hand that utilizes the simplicity of an antipodal grasp and satisfies force closure condition. The algorithm was tested in Gazebo simulation with sample objects that were modeled after selected household items.