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