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.
Highlights
For an Unmanned Ground Vehicle (UGV) working in dynamic environments, path planning remains one of the most problematic issues to solve [1]
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
To test the use of neural networks for object recognition during autonomous navigation, we decided to carry out an experimental investigation conducted by using the five SRF05 ultrasonic sensors and a small UGV, used for the identification and control applications [47,48,49]
Summary
For an Unmanned Ground Vehicle (UGV) working in dynamic environments, path planning remains one of the most problematic issues to solve [1]. In Al-Mayyahi et al (2014), the authors used an adaptive neuro-fuzzy inference system for navigation purposes by fusing sensor information Such a system was made of four controllers: two are used for angular velocity regulation for reaching the target position and the other two are used for obstacle avoidance [7,8,9,10]. We decided to use low-cost sensors together with neural network algorithms for object recognition for unmanned vehicles. To test the use of neural networks for object recognition during autonomous navigation, we decided to carry out an experimental investigation conducted by using the five SRF05 ultrasonic sensors and a small UGV, used for the identification and control applications [47,48,49].
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