Application of Neural Data Processing in Autonomous Model Platform—A Complex Review of Solutions, Design and Implementation

Mateusz Malarczyk, Jules-Raymond Tapamo and Marcin Kaminski
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Mateusz Malarczyk: Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-372 Wroclaw, Poland
Jules-Raymond Tapamo: School of Engineering, University of KwaZulu-Natal, Durban 4041, South Africa
Marcin Kaminski: Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-372 Wroclaw, Poland

Energies, 2022, vol. 15, issue 13, 1-22

Abstract: One of the bottlenecks of autonomous systems is to identify and/or design models and tools that are not too resource demanding. This paper presents the concept and design process of a moving platform structure–electric vehicle. The objective is to use artificial intelligence methods to control the model’s operation in a resource scarce computation environment. Neural approaches are used for data analysis, path planning, speed control and implementation of the vision system for road sign recognition. For this purpose, multilayer perceptron neural networks and deep learning models are used. In addition to the neural algorithms and several applications, the hardware implementation is described. Simulation results of systems are gathered, data gathered from real platform tests are analyzed. Experimental results show that low-cost hardware may be used to develop an effective working platform capable of autonomous operation in defined conditions.

Keywords: neural classifier; vision system; neural speed controller; distance measurement; deep learning; control system; programmable devices; autonomous vehicles (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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