Automated Path Search and Optimization of Robotic Motion Using Hybrid ART-SOM Neural Networks

  • V. M. AparanjiEmail author
  • Uday V. Wali
  • R. Aparna
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 34)


This paper proposes a new type of unsupervised, path optimizing artificial neural network (ANN) suitable for autonomous motion control of multi-joint robotic mechanisms with arbitrary degrees of freedom (DoF). The ANN can search through the robot’s workspace and select an optimal path avoiding obstacles, among several possible paths. This approach does not require computation of nonlinear inverse kinematic expressions generally used for such mechanisms. The proposed ANN combines features of adaptive resonance theory (ART) and self-organizing maps (SOMs). It is a sparse hierarchical multilayer deep learning network with specific features implemented at each layer. Cells in lower levels classify input using a ART/SOM hybrid structure. Higher levels will successively identify and optimize paths that can be used to solve motion problems. The paper describes the cellular automata required to implement the path optimizing network. These ANNs have been implemented using R simulation language. Results for various types of joint systems are presented.


Adaptive resonance theory (ART) Artificial neural networks (ANNs) Deep learning Motion control Self-organizing maps (SOMs) 



The authors would like to thank Siddaganga Institute of Technology, Tumakuru, C-Quad, Belagavi and KLE Dr. M S Sheshgiri College of Engineering & Technology, Belagavi for all the support.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of E&CESiddaganga Institute of TechnologyTumakuruIndia
  2. 2.Department of E&CEKLE Dr. MSS CETBelagaviIndia
  3. 3.Department of ISESiddaganga Institute of TechnologyTumakuruIndia

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