Formation of the quadcopter flight path under overland monitoring using neuro-fuzzy modeling methods.

Abstract

Abstract. Some territories where economic activity is carried out are characterized by the presence of mountainі and forests. To provide information support for the development of infrastructure and agriculture in these areas, in some cases, there is required overland monitoring with unmanned technologies. In this regard, an algorithm for the formation of a 3D trajectory of a quadcopter during overland piloting in a mountainous and wooded landscape is proposed, which implies autonomous maneuvering to overcome possible obstacles. As a basic model, it is proposed to use a fuzzy inference system with input characteristics in the form of linguistic variables that reflect fuzzy sectors of space, within which the presence of obstacles and the distance to them are interpreted verbally, i.e., in the form of terms of corresponding input linguistic variables. Overcoming obstacles is supposed to be performed on the basis of fuzzy conclusions of the proposed system, formulated as terms of output linguistic variables which reflect changes in the angle of rotation in the horizontal plane, flight altitude, and traverse speed of the quadcopter. The paper analyzes the results of the model behavior for different scenarios of the terms of the input linguistic variables. For the operational formation of the quadcopter flight path, it is also proposed to use neural network modeling tools. Due to its ability to adapt to new conditions and requirements, the neural network model can become an important tool in ensuring the autonomous flight of a quadcopter under overland monitoring. For appropriate training of the three-layer feedforward neural network, a sufficiently large number of quadcopter behavior scenarios are used, which were generated by the fuzzy inference system relative to the overcoming of possible obstacles in five sectors of the survey.