An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network

The solution of robot inverse kinematics has a direct impact on the control accuracy robot. Conventional methods, such as numerical solution, algebraic and geometric have insufficient speed accuracy, process is complicated. Due to mapping ability neural network, use networks solve problems attracted widespread attention. However, it slow convergence low accuracy. This paper proposes FOA optimized BP network algorithm kinematics. It overcomes shortcomings speed, easy fall into local minima when using experimental results show that trained kinematics, maximum error range output joint angle [−0.04686, 0.1271]. smaller than ordinary PSO algorithm. Using can improve