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

Edeh Michael Onyema, Surjeet Dalal, Celestine Iwendi, Bijeta Seth, Nwogbe Odinakachi and Anyalor Maureen Chichi

International Journal of Services, Economics and Management, 2024, vol. 15, issue 5, 497-519

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.

Keywords: industrial robots; control movement; machine learning; neural network; extremely boosted neural network; XBNet. (search for similar items in EconPapers)
Date: 2024
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