NDLI: A neuro-simulated annealing approach to the inverse kinematics solution of redundant robotic manipulators

Content Provider	Springer Nature Link
Author	Köker, Raşit
Copyright Year	2012
Abstract	The neural-network-based inverse kinematics solution is one of the recent topics in the robotics because of the fact that many traditional inverse kinematics problem solutions such as geometric, iterative and algebraic are inadequate for redundant robots. However, since the neural networks work with an acceptable error, the error at the end of inverse kinematics learning should be minimized. In this study, simulated annealing (SA) algorithm was used together with the neural-network-based inverse kinematics problem solution robots to minimize the error at the end effector. The solution method is applied to Stanford and Puma 560 six-joint robot models to show the efficiency. The proposed algorithm combines the characteristics of neural network and an optimization technique to obtain the best solution for the critical robotic applications. Three Elman neural networks were trained using separate training sets and different parameters, since one of them can give better results than the others can. The best result is selected within three neural network results by computing the end effector error via direct kinematics equation of the robotic manipulator. The decimal part of the neural network result was improved up to 10 digits using simulated annealing algorithm. The obtained best solution is given to the simulated annealing algorithm to find the best-fitting 10 digits for the decimal part of the solution. The end effector error was reduced significantly.
Starting Page	507
Ending Page	515
Page Count	9
File Format	PDF
ISSN	01770667
Journal	Engineering with Computers
Volume Number	29
Issue Number	4
e-ISSN	14355663
Language	English
Publisher	Springer London
Publisher Date	2012-10-02
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Elman network Inverse kinematics solution Simulated annealing Error minimization Neural networks Computer-Aided Engineering (CAD, CAE) and Design Math. Applications in Chemistry Systems Theory, Control Calculus of Variations and Optimal Control; Optimization Mechanics ApplicationMathematics/Computational Methods of Engineering
Content Type	Text
Resource Type	Article
Subject	Engineering Computer Science Applications Modeling and Simulation Software

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