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Management and prediction of navigation of industrial robots based on neural network

Author

Listed:
  • Edeh Michael Onyema
  • Surjeet Dalal
  • Celestine Iwendi
  • Bijeta Seth
  • Nwogbe Odinakachi
  • Anyalor Maureen Chichi

Abstract

In the past, a robotic arm needed to be taught to carry out certain tasks, such as selecting a single object type from a fixed location and orientation. Neural networks have autonomous abilities that are being deployed to aid the development of robots and also improve their navigation accuracy. Maximising the potentials of neural network as shown in this study enhances the positioning and movement targets of industrial robots. The study adopted an architecture called extremely boosted neural network (XBNet) trained using a unique optimisation approach (boosted gradient descent for tabular data - BGDTD) that improves both its interpretability and performance. Based on the analysis of the simulations, the result demonstrates accuracy and precision. The study would contribute significantly to the advancement of robotics and its efficiency.

Suggested Citation

  • Edeh Michael Onyema & Surjeet Dalal & Celestine Iwendi & Bijeta Seth & Nwogbe Odinakachi & Anyalor Maureen Chichi, 2024. "Management and prediction of navigation of industrial robots based on neural network," International Journal of Services, Economics and Management, Inderscience Enterprises Ltd, vol. 15(5), pages 497-519.
    • Handle: RePEc:ids:injsem:v:15:y:2024:i:5:p:497-519
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