Safety analysis via forward kinematics of delta parallel robot using machine learning.

• GABP can effectively address the forward kinematics problems of Delta robot. • If the tolerance prediction error of ±0.05 mm is acceptable to the high precision control of Delta parallel robot system, mobile platform endpoint of the Delta robot system can achieve 97.75% accurate positing according to the Predicted Coordinate Pairs. • The proposed model overcomes weakness of traditional forward kinematics methods. Aiming at solving the problems of complex forward kinematics of Delta parallel robot and multiple solutions, and further improving celerity and accuracy of positioning for the spatial pose of the manipulator end effecter, in this paper, we present a method to kinematics solution of Delta parallel robot based on BP neural network. Taking the three-degree-of-freedom Delta parallel robot as the research object, according to analysis of its the kinematics principle, the basic BP neural network model and the optimized BP neural network model for kinematics solution of Delta parallel robot are simulated by using MATLAB, respectively. The results indicate that using BP neural network model improved by Genetic Algorithms to address the forward kinematics problems of Delta parallel robot is feasible, which can achieve the requirement of higher celerity and accuracy of Delta parallel robot control and avoid the shortcomings of traditional methods to a certain extent, furthermore, ensures the reliability of production safety. [ABSTRACT FROM AUTHOR]