Inverse kinematics solution for redundant robot manipulator using combination of GA and NN

Abstract EN

demonstration of the inverse kinematics is a very complex problem for redundant robot manipulator.

This paper presents the solution of inverse kinematics for one of redundant robots manipulator (three link robot) by combing of two intelligent algorithms GA (Genetic Algorithm) and NN (Neural Network).

The inputs are position and orientation of three link robot.

These inputs are entering to Back Propagation Neural Network (BPNN).

The weights of BPNN are optimized using continuous GA.

The (Mean Square Error) MSE is also computed between the estimated and desired outputs of joint angles.

In this paper, the fitness function in GA is proposed.

The sinwave and circular for three link robot end effecter and desired trajectories are simulated by MATLAB program.

Joint angles and end effecter positions of robot results values of circular trajectory are better than joint angles end effecter positions of robot results values of NN work in another paper.

Three link redundant robot workspace is also simulated.

The outputs results of best three joint angles are evaluated for two trajectories sinwave and circular, with 50 generations the algorithm is fast.

This paper presents the simulations results that are obtained based on MATLAB R2010b program.