Neural network control system of motion of the robot in the environment with obstacles

Abstract

The article deals with the combined motion control system which provides an autonomous movement of the robot in an uncertain environment. The motion planning level is implemented on a cascade neural network of deep learning. The proposed structure of the network allows decomposing the task of planning a path to the task of deciding whether to maneuver and the task of selecting a direction to bypass an obstacle. The motion control level is implemented in the form of a hybrid system that includes the neural network correction of the path, and the algorithm for avoiding collisions, built on the basis of unstable modes. The control system was modeled and as the result of modeling the quality of control system was obtained. The results of experiments confirming the performance of the control system are presented. It is proposed to classify the environment of operation of the robot according to the complexity of the current situation, depending on the need for maneuver. The environment is classified into complexity classes, the number of which depends on the number of active network cascades.