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Computer Science > Robotics

arXiv:2208.09165 (cs)
[Submitted on 19 Aug 2022]

Title:ACO based Adaptive RBFN Control for Robot Manipulators

Authors:Sheheeda Manakkadu, Sourav Dutta
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Abstract:This paper describes a new approach for approximating the inverse kinematics of a manipulator using an Ant Colony Optimization (ACO) based RBFN (Radial Basis Function Network). In this paper, a training solution using the ACO and the LMS (Least Mean Square) algorithm is presented in a two-phase training procedure. To settle the problem that the cluster results of k-mean clustering Radial Basis Function (RBF) are easy to be influenced by the selection of initial characters and converge to a local minimum, Ant Colony Optimization (ACO) for the RBF neural networks which will optimize the center of RBF neural networks and reduce the number of the hidden layer neurons nodes is presented. The result demonstrates that the accuracy of Ant Colony Optimization for the Radial Basis Function (RBF) neural networks is higher, and the extent of fitting has been improved.
Subjects: Robotics (cs.RO)
Cite as: arXiv:2208.09165 [cs.RO]
  (or arXiv:2208.09165v1 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2208.09165

Submission history

From: Sourav Dutta [view email]
[v1] Fri, 19 Aug 2022 06:21:04 UTC (282 KB)
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