Artificial Neural Network for Stable Robotic Grasping

The optimal grasping point of the object varies depending on the shape of the object, such as the weight, the material, the grasping contact with the robot hand, and the grasping force. In order to derive the optimal grasping points for each object by a three fingered robot hand, optimal point and posture have been derived based on the geometry of the object and the hand using the artificial neural network. The optimal grasping cost function has been derived by constructing the cost function based on the probability density function of the normal distribution. Considering the characteristics of the object and the robot hand, the optimum height and width have been set to grasp the object by the robot hand. The resultant force between the contact area of the robot finger and the object has been estimated from the grasping force of the robot finger and the gravitational force of the object. In addition to these, the geometrical and gravitational center points of the object have been considered in obtaining the optimum grasping position of the robot finger and the object using the artificial neural network. To show the effectiveness of the proposed algorithm, the friction cone for the stable grasping operation has been modeled through the grasping experiments.

[1]  Chao Yang,et al.  Robotic grasping using visual and tactile sensing , 2017, Inf. Sci..

[2]  Sung-Hyun Han,et al.  A study on gripping control of robotic hand with ten joints for cooperative working , 2014, 2014 14th International Conference on Control, Automation and Systems (ICCAS 2014).

[3]  김성현,et al.  퍼지-뉴럴 융합을 이용한 로보트 Gripper의 힘 제어기 ( Force Controller of the Robot Gripper Using Fuzzy-Neural Fusion ) , 1991 .

[4]  Tsuneo Yoshikawa,et al.  Multifingered robot hands: Control for grasping and manipulation , 2010, Annu. Rev. Control..

[5]  Wisnu Jatmiko,et al.  Learning semantic segmentation score in weakly supervised convolutional neural network , 2015, 2015 International Conference on Computers, Communications, and Systems (ICCCS).

[6]  Joel Lehman,et al.  Grasping novel objects with a dexterous robotic hand through neuroevolution , 2014, CICA.

[7]  Danica Kragic,et al.  Dexterous grasping under shape uncertainty , 2016, Robotics Auton. Syst..

[8]  Mathieu Aubry,et al.  Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Byung-Ju Yi,et al.  Optimal Grasp Planning of Object Based on Weighted Composite Grasp Index , 2000 .

[10]  Quoc V. Le,et al.  Grasping novel objects with depth segmentation , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Mark R. Cutkosky,et al.  On grasp choice, grasp models, and the design of hands for manufacturing tasks , 1989, IEEE Trans. Robotics Autom..

[12]  Jang-Myung Lee,et al.  Singlarity and Collision Avoidance Path Planning based upon Artificial Potential Field and Manipulability Measure , 2018 .

[13]  Saïd Zeghloul,et al.  A real-time strategy for dexterous manipulation: Fingertips motion planning, force sensing and grasp stability , 2012, Robotics Auton. Syst..

[14]  Kazuaki Iwata,et al.  Static analysis of deformable object grasping based on bounded force closure , 1996, Proceedings of IEEE International Conference on Robotics and Automation.