Mechatronics Robot Navigation using Machine Learning through Prolog Programming Language

Authors

  • Shubhangee K. Varma Lecturer, Department of Electronics and Telecommunication, Cusrow Wadia Institute of Technology, Pune, Maharashtra, India
  • Ashok S. Chandak Vice Principal and Head, Department of Electronics and Telecommunication, Cusrow Wadia Institute of Technology, Pune, Maharashtra, India
  • Prakash P.W. Wani Director, Department of Electronics and Telecommunication, Trinity College of Engineering and Research, Pune, Maharashtra, India
  • Arunkumar B. Patki Emeritus professor, Department of Electronics and Telecommunication, College of Engineering, Pune, Maharashtra, India

Keywords:

MLP (multilayer perceptron), BP (back-propagation), PL (Prolog), AI (Artificial Intelligence), machine learning

Abstract

A basic decision-making system is developed in this paper using Neural Network in Machine learning
to explore a robot in concealed condition. The robot can move out of explicit labyrinths effectively
through modifying its bearing and speed persistently via the neural system model for machine
learning. Over the past several years, navigation tasks for mobile robots have been widely studied.
There have been many attempts to introduce the usage of machine learning algorithms. Excellent
performance in various fields like robot navigation, image processing, makes the Deep learning
techniques special. But a considerable amount of data is required for training deep learning models.
Also, the results of deep learning methods may be difficult to interpret for researchers. To address
this issue, a novel model for mobile robot navigation using deep reinforcement learning is proposed
in this paper. The results show that the robot could explore independent in obscure situations. Prolog
programing language is used to simulate the robot code and demonstrated that it is simple way to
simulate the code. In this paper, Machine learning is done using prolog language which is effective
way to write various artificial intelligence code and simulate. Autonomous navigation using neural
network is explained in this paper with suitable example. Back-propagation neural network is also
elaborated with diagram and SWI Prolog language is explained and used to write the Robot code.
The result of prolog language robot code is also explained in this paper. Hence, Prolog language is
efficient programming logic language to write and simulate artificial intelligence codes.

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https://www.cpp.edu/~jrfisher/www/prolog_tutorial/2_19.html

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Published

2021-05-29

Issue

Vol. 8 No. 1 (2021)

Section

Articles

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