Volume 8, No. 1, January 2019

General Information

  • ISSN: 2278-0149 (Online)
  • Abbreviated Title:  Int. J. Mech. Eng. Robot. Res.  
  • Editor-in-Chief: ​Prof Richard (Chunhui) Yang, Western Sydney University, Australia
  • Associate Editor: Prof. B.V. Appa Rao, Andhra University; Prof. Ian McAndrew, Capitol Technology University, USA
  • Managing Editor: Murali Krishna. B
  • DOI: 10.18178/ijmerr
  • Abstracting/Indexing: Scopus (since 2016), CNKI, Google Scholar, Crossref, etc.
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International Journal of Mechanical Engineering and Robotics Research
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Artificial Neural Network Based Path Planning of Excavator Arm

Nga T. T.Vu, Do D. Bui, and Hieu T. Tran
Department of Automatic Control, Hanoi University of Science and Technology, Hanoi, Vietnam

Abstract—This paper presents a solution in path planning for a robotic arm based on the artificial neural network (ANN) architecture, particularly a Static (Feedforward) Neural Network (SNN). The inputs of the network are the sample sets that are obtained from some specific requirements of the desired trajectory. After training, the outputs of the network are the smooth curves that will be used as the reference trajectory for the joints of the excavator arm. The capabilities of the designed neural network in solving the path planning problems are clearly demonstrated through a simulation conducted with a complex trajectory for the excavator. 

Index Terms—Artificial Neural Network (ANN), Static Neural Network, Feedforward Neural Network (FFNN), Excavator, Manipulator

Cite: Nga T. T.Vu, Do D. Bui, and Hieu T. Tran, "Artificial Neural Network Based Path Planning of Excavator Arm," International Journal of Mechanical Engineering and Robotics Research, Vol. 8, No. 1, pp. 12-17, January 2019. DOI: 10.18178/ijmerr.8.1.12-17