Mechatronics Robot Navigation using Machine Learning through Prolog Programming Language

Shubhangee K. Varma; Ashok S. Chandak; Prakash P.W. Wani; Arunkumar B. Patki

Abstract

A basic decision-making system is developed in this paper using Neural Network in Machine learningto explore a robot in concealed condition. The robot can move out of explicit labyrinths effectivelythrough modifying its bearing and speed persistently via the neural system model for machinelearning. 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. Excellentperformance in various fields like robot navigation, image processing, makes the Deep learningtechniques 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 addressthis issue, a novel model for mobile robot navigation using deep reinforcement learning is proposedin this paper. The results show that the robot could explore independent in obscure situations. Prologprograming language is used to simulate the robot code and demonstrated that it is simple way tosimulate the code. In this paper, Machine learning is done using prolog language which is effectiveway to write various artificial intelligence code and simulate. Autonomous navigation using neuralnetwork is explained in this paper with suitable example. Back-propagation neural network is alsoelaborated 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 isefficient programming logic language to write and simulate artificial intelligence codes.

Abstract

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