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

Authors: Edeh Michael Onyema; Surjeet Dalal; Celestine Iwendi; Bijeta Seth; Nwogbe Odinakachi; Anyalor Maureen Chichi

Addresses: Department of Mathematics and Computer Science, Coal City University, Enugu, Nigeria; Adjunct Faculty, Saveetha School of Engineering, Saveetha Institute of Medical Sciences, Chennai, India ' Department of Computer Science and Engineering, Amity University Haryana, Gurugram, Haryana, India ' School of Creative Technologies, University of Bolton, Bolton BL3 5AB, UK ' Department of Computer Science and Engineering, B.M. Institute of Engineering and Technology, Sonipat, Haryana, India ' Department of Mathematics and Computer Science, Spiritan University, Abia, Nigeria ' Department of Management Sciences, Coal City University, Enugu – Abakaliki Rd, Emene 400104, Enugu, Nigeria

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.

DOI: 10.1504/IJSEM.2024.140940

International Journal of Services, Economics and Management, 2024 Vol.15 No.5, pp.497 - 519

Received: 30 Sep 2022
Accepted: 15 Nov 2022
Published online: 04 Sep 2024 *

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