ALGORITHM FOR CONSTRUCTING THE ROUTE OF A ROBOTIC COMPLEX USING THE FUZZY LOGIC METHOD

Abstract

This article presents the mathematical justification of a path planning algorithm for a mobile robotic complex (MRC) following an operator during autonomous control tasks using artificial intelligence (AI). A proposed approach implements a "follow me" autonomous following task for the MRC. A pursuit method is selected as the primary method, ensuring the MRC follows the leading operator at a specified distance. The MRC's movement simulation is performed in a moving coordinate system to more accurately describe the movement of a material point along a curvilinear trajectory. The input data consists of two dynamic arrays containing information about the distance from the MRC's camera to the leading operator and the course angle between the complex's longitudinal axis and the line of sight. Path planning is performed with a delay, after the leading operator has conditionally taken one step away from the robot. The introduction of fuzziness in the control process implies evaluating actions and reactions with a set of terms that are associated with a certain degree of confidence with specific intervals of physical quantities. Based on this approach, an algorithm was developed and implemented in the Python programming environment using the Skfuzzy library's built-in fuzzy logic functions. Simulation modeling was conducted to evaluate the accuracy of the target function implementation. Analysis of the results revealed the main advantages of using fuzzy logic for automation tasks compared to traditional approaches in automatic control theory