Neural Network Method of Controlling Self-Collisions of Multilink Manipulators Based on the Solution of the Classification Problem

Abstract

The problem of self-collisions of manipulators with several links installed on a robot can arise when they work together in one zone. To prevent self-collisions, it is necessary to develop methods for their detection and their subsequent inclusion in control algorithms. This paper proposes an approach for determining the occurrence of self-collisions of manipulators using the Artificial Neural Networks approach. In contrast to the regression problem, this article proposes a classification approach. The effectiveness of the proposed approach was tested on robots with multilink manipulators “Ichtiandr” and SAR-401 and their simulators. Self-collision detection using the proposed method is much faster than using the traditional approach of solving the inverse kinematics problem without loss of accuracy. The problem was solved by constructing various Artificial Neural Networks and then checking the accuracy of the solution. A comparative analysis of Artificial Neural Networks was carried out and as a result, the Artificial Neural Networks approach showing the best accuracy was selected. The problem was solved for a robot with two manipulators. The resulting solution can be extended to a larger number of manipulators installed on the robot.

Keywords

• manipulator’s self-collision
• multilink dual-manipulator robot
• classification
• artificial neural networks