Abstract
Grasping of objects has been a challenging task for robots. The complex grasping task can be defined as object contact control and manipulation subtasks. In this paper, object contact control subtask is defined as the ability to follow a trajectory accurately by the fingers of a gripper. The object manipulation subtask is defined in terms of maintaining a predefined applied force by the fingers on the object. A sophisticated controller is necessary since the process of grasping an object without a priori knowledge of the object's size, texture, softness, gripper, and contact dynamics is rather difficult. Moreover, the object has to be secured accurately and considerably fast without damaging it. Since the gripper, contact dynamics, and the object properties are not typically known beforehand, an adaptive critic neural network (NN)-based hybrid position/force control scheme is introduced. The feedforward action generating NN in the adaptive critic NN controller compensates the nonlinear gripper and contact dynamics. The learning of the action generating NN is performed on-line based on a critic NN output signal. The controller ensures that a three-finger gripper tracks a desired trajectory while applying desired forces on the object for manipulation. Novel NN weight tuning updates are derived for the action generating and critic NNs so that Lyapunov-based stability analysis can be shown. Simulation results demonstrate that the proposed scheme successfully allows fingers of a gripper to secure objects without the knowledge of the underlying gripper and contact dynamics of the object compared to conventional schemes.
Recommended Citation
G. Galan and J. Sarangapani, "Adaptive Critic Neural Network-Based Object Grasping Control using a Three-finger Gripper," IEEE Transactions on Neural Networks, Institute of Electrical and Electronics Engineers (IEEE), Jan 2004.
The definitive version is available at https://doi.org/10.1109/TNN.2004.824407
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Sponsor(s)
National Science Foundation (U.S.)
Techathlon
Texas Space Grant Consortium
Keywords and Phrases
Adaptive Controller; Adaptive Critic; Neural Networks (NNs); Object Grasping; Three-Finger Gripper
International Standard Serial Number (ISSN)
1045-9227
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2004 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2004
Included in
Computer Sciences Commons, Electrical and Computer Engineering Commons, Operations Research, Systems Engineering and Industrial Engineering Commons
