Robot motion similarity analysis using an FNN learning mechanism

doi:10.1016/S0165-0114(00)00081-6

Fuzzy Sets and Systems

Volume 124, Issue 2, 1 December 2001, Pages 155-170

https://doi.org/10.1016/S0165-0114(00)00081-6 Get rights and content

Abstract

Learning controllers are usually subordinate to conventional controllers in governing multiple-joint robot motion, in spite of their ability to generalize, because learning space complexity and motion variety require them to consume excessive amount of memory when they are employed as major roles in motion governing. We propose using a fuzzy neural network (FNN) to learn and analyze robot motions so that they can be classified according to similarity. After classification, the learning controller can then be designed to govern robot motions according to their similarities without consuming excessive memory resources.

References (22)

C.-T. Lin
A neural fuzzy control system with structure and parameter learning
Fuzzy Sets and Systems
(1995)
B.A.M. Wakileh et al.
Use of fuzzy logic in robotics
Comput. Ind.
(1988)
K.Y. Young et al.
An approach to simplify the learning space for robot learning control
Fuzzy Sets and Systems
(1998)
J.S. Albus
A new approach to manipulator controlthe cerebellar model articulation controller (CMAC)
ASME J. Dyn. Systems Meas. Control
(1975)
S. Arimoto, Robustness of learning control for robot manipulators, IEEE Internat. Conf. Robotics Automat, 1990, pp....
H.R. Berenji et al.
Learning and tuning fuzzy logic controllers through reinforcements
IEEE Trans. Neural Networks
(1992)
C.T. Chao et al.
Simplification of fuzzy-neural systems using similarity analysis
IEEE Trans. Systems Man Cybernet. Part B: Cybernet.
(1996)
J.S.R. Jang
ANFISadaptive-network-based fuzzy inference system
IEEE Trans. Systems Man Cybernet.
(1993)
S. Jung, T.C. Hsia, On reference trajectory modification approach for cartesian space neural network control of robot...
A. Karakasoglu et al.
Identification and decentralized adaptive control using dynamical neural networks with application to robotic manipulators
IEEE Trans. Neural Networks
(1993)

M. Kawato et al.

Hierarchical neural network model for voluntary movement with application to robotics

IEEE Control Systems Mag.

(1988)

Cited by (5)

Intelligent task planning and action selection of a mobile robot in a multi-agent system through a fuzzy neural network approach
2010, Engineering Applications of Artificial Intelligence
Citation Excerpt :
In the implementation level, emergent cooperative behaviours of the robots are realized through various control schemes (Jeong and Lee,1997; Fierro and Lewis, 1998). This paper proposes a fuzzy neural network based approach (Chao et al., 1996; Young and Wang, 2001) for the intelligent action selection mechanism of a mobile robot in a MAS. This paper is organized as follows.
This paper proposes an intelligent task planning and action selection mechanism for a mobile robot in a robot soccer system through a fuzzy neural network approach. The proposed fuzzy neural network system is developed through the two dimensional fuzzification of the soccer field. A five layer fuzzy neural network system is trained through error back propagation learning algorithm to impart a strategy based action selection. The action selection depends on the field configuration, and the emergence of a particular field configuration results from the game dynamics. Strategy of the robot changes when the configuration of the objects in the field changes. The proposed fuzzy neural network structure is flexible to accommodate all possible filed configurations. Simulation results indicate that the proposed approach is simple and has the capability in coordinating the multi-agent system through selection of sensible actions.
Robot motion classification from the standpoint of learning control
2004, Fuzzy Sets and Systems
In robot learning control, the learning space for executing the general motions of multi-joint robot manipulators is very complicated. Thus, when the learning controllers are employed as major roles in motion governing, the motion variety requires them to consume excessive amount of memory. Therefore, in spite of their ability to generalize, the learning controllers are usually used as subordinates to conventional controllers or the learning process needs to be repeated each time a new trajectory is encountered. To simplify learning space complexity, we propose, from the standpoint of learning control, that robot motions be classified according to their similarities. The learning controller can then be designed to govern groups of robot motions with high degrees of similarity without consuming excessive memory resources. Motion classification based on using the PUMA 560 robot manipulator demonstrates the effectiveness of the proposed scheme.
Robot motion command simplification and scaling
2002, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Task Allocation on Layered Multiagent Systems: When Evolutionary Many-Objective Optimization Meets Deep Q-Learning
2021, IEEE Transactions on Evolutionary Computation
Fuzzy neural network water-mixed control system based on hybrid algorithm
2010, Proceedings - 2010 6th International Conference on Natural Computation, ICNC 2010

^☆: This work was supported in part by the National Science Council, Taiwan, under grant NSC 87-2213-E-009-145.

View full text

Robot motion similarity analysis using an FNN learning mechanism☆

Abstract

Fuzzy Sets and Systems

Comput. Ind.

Fuzzy Sets and Systems

A new approach to manipulator controlthe cerebellar model articulation controller (CMAC)

ASME J. Dyn. Systems Meas. Control

Learning and tuning fuzzy logic controllers through reinforcements

IEEE Trans. Neural Networks

Simplification of fuzzy-neural systems using similarity analysis

IEEE Trans. Systems Man Cybernet. Part B: Cybernet.

ANFISadaptive-network-based fuzzy inference system

IEEE Trans. Systems Man Cybernet.

Identification and decentralized adaptive control using dynamical neural networks with application to robotic manipulators

IEEE Trans. Neural Networks

Hierarchical neural network model for voluntary movement with application to robotics

IEEE Control Systems Mag.