Abstract
A 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.
Highlights
One of the main problems for serial-chain manipulators robots is the inverse kinematics problem, where, it needs to find the values of the joint positions given the position and orientation of the end-effector relative to the base
Extreme Learning Machine (ELM) is used to train the neural network [2]. Another neural network solution of the inverse kinematics problem is used for a threejoint robotic manipulator where, the neural network is trained until the error is acceptable [3]
The proposed flowchart of GA and Back Propagation Neural Network (BPNN) algorithm is illustrated as shown in Figure 6 and the two difficult trajectories results are simulated, one for the sinwave trajectory and the second for the circular trajectory, but the researchers Md and Tania [10] are just presented GA and NN algorithm but they did not evaluated any results of any trajectory
Summary
One of the main problems for serial-chain manipulators robots is the inverse kinematics problem, where, it needs to find the values of the joint positions given the position and orientation of the end-effector relative to the base. For solving the inverse kinematics problem, there are many solutions [1]. Neural networks have been widely applied to solve the robot manipulator inverse kinematics problems. Extreme Learning Machine (ELM) is used to train the neural network [2] Another neural network solution of the inverse kinematics problem is used for a threejoint robotic manipulator where, the neural network is trained until the error is acceptable [3]. An Artificial Neural Network (ANN) model is presented to find the inverse kinematics solution of a SCARA manipulator [4]. The only genetic algorithm is used to optimization the inverse kinematics of three degrees of freedom model industrial robot manipulator and improving the efficiency of the model [6]
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