Machine Learning Control Synthesis by Symbolic Regression for Avoidance of Arbitrary Positioned Obstacles

The problem of control synthesis for a mobile robot with phase constraints in the form of an arbitrarily located obstacle is formulated. To solve the problem, a numerical method of machine learning based on symbolic regression is used. According to the approach, in addition to its state, the object receives information about the distance to the obstacle and the direction of its location. As a result of solving the synthesis problem, we obtain a nonlinear feedback control function that provides a better estimate of the obstacle bypass according to a given criterion. An example for a mobile robot with a differential drive is considered.