Obstacle Avoidance System for Unmanned Ground Vehicles by Using Ultrasonic Sensors

TLDR

Artificial intelligence, encompassing algorithmic techniques for machine reasoning, has enabled the emergence of self‑driving vehicles and is increasingly essential for future precision agriculture amid projected global population growth. The study aims to enhance precision agriculture by integrating autonomous agricultural machines. Using MATLAB’s Neural Network Toolbox, the authors designed a supervised neural network trained on ultrasonic sensor data to classify and recognize patterns, enabling retrofitting of existing agricultural machinery.

Abstract

Artificial intelligence is the ability of a computer to perform the functions and reasoning typical of the human mind. In its purely informatic aspect, it includes the theory and techniques for the development of algorithms that allow machines to show an intelligent ability and/or perform an intelligent activity, at least in specific areas. In particular, there are automatic learning algorithms based on the same mechanisms that are thought to be the basis of all the cognitive processes developed by the human brain. Such a powerful tool has already started to produce a new class of self-driving vehicles. With the projections of population growth that will increase until the year 2100 up to 11.2 billion, research on innovating agricultural techniques must be continued. In order to improve the efficiency regarding precision agriculture, the use of autonomous agricultural machines must become an important issue. For this reason, it was decided to test the use of the “Neural Network Toolbox” tool already present in MATLAB to design an artificial neural network with supervised learning suitable for classification and pattern recognition by using data collected by an ultrasonic sensor. The idea is to use such a protocol to retrofit kits for agricultural machines already present on the market.