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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 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.


Keywords


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

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References


Ghazal S, Khan US, Mubasher Saleem MM, Rashid N, Iqbal J. Human activity recognition using 2D skeleton data and supervised machine learning. IET Image Process. 2019;13(13):2572–8. doi: 10.1049/iet-ipr.2019.0030.

Gebre BA, Pochiraju KV. Machine learning aided design and analysis of a novel magnetically coupled ball drive. IEEE ASME Trans Mechatron. 2019;24(5):1942–53. doi: 10.1109/TMECH.2019.2929956.

Yang G, Deng J, Pang G, Zhang H, Li J, Deng B, Pang Z, Xu J, Jiang M, Liljeberg P, Xie H, Yang H. An IoT-enabled stroke rehabilitation system based on smart wearable armband and machine learning. IEEE J Transl Eng Health Med. 2018;6:2100510. doi: 10.1109/JTEHM.2018.2822681, PMID 29805919.

Murrell N, Bradley R, Bajaj N, Whitney JG, Chiu GT-C. A method for sensor reduction in a supervised machine learning classification system. IEEE ASME Trans Mechatron. 2018;24(1):197–206. doi: 10.1109/TMECH.2018.2881889.

Aksjonov A, Nedoma P, Vodovozov V, Petlenkov E, Herrmann M. Detection and evaluation of driver distraction using machine learning and fuzzy logic. IEEE Trans Intell Transp Syst. 2018;20(6):2048–59. doi: 10.1109/TITS.2018.2857222.

Janssens O, Van de Walle R, Loccufier M, Van Hoecke S. Deep learning for infrared thermal image based machine health monitoring. IEEE ASME Trans Mechatron. 2017;23(1):151–9. doi: 10.1109/TMECH.2017.2722479.

Tan DP, Ji SM, Jin MS. Intelligent computer-aided instruction modeling and a method to optimize study strategies for parallel robot instruction. IEEE Trans Educ. 2012;56(3):268–73. doi: 10.1109/TE.2012.2212707.

Zhao Z, Zhang X, Li W, Hu X, Qu X, Cao X, Liu Y, Lu J. Applying machine learning to identify autism with restricted kinematic features. IEEE Access. 2019;7:157614–22. doi: 10.1109/ACCESS.2019.2950030, PMID 157614.

Li P, Hou X, Duan X, Yip H, Song G, Liu Y. Appearance-based gaze estimator for natural interaction control of surgical robots. IEEE Access. 2019;7:25095–110. doi: 10.1109/ACCESS.2019.2900424.

Ying Wang, Haoxiang Lang, de Silva CW. A hybrid visual servo controller for robust grasping by wheeled mobile robots. IEEE ASME Trans Mechatron. 2009;15(5):757–69. doi: 10.1109/TMECH.2009.2034740.

Kishan VS, Fault PA B. Detection in combinational circuit (full adder)using. Prolog. 2019;11.

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


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