Abstract
This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles.
Highlights
Artificial intelligence has become the most modern technology of robotic control
The inverse kinematics of three DOF robotic arm was solved by multilayer network inversion method; the joint angles were estimated for given end effector position in a simulation of three-link robotic arm
The inverse kinematics solution for robot motion is achieved by proposed artificial neural network (ANN) and traditional ANN
Summary
Artificial intelligence has become the most modern technology of robotic control. It has many advantages in performance such as precise control and less computing time, in addition to overcoming some mathematical problems in motion and path generation. The inverse kinematics of three DOF robotic arm was solved by multilayer network inversion method; the joint angles were estimated for given end effector position in a simulation of three-link robotic arm. A neural network and genetic algorithms were used together to solve the inverse kinematics problem of the nonsurgical robotic manipulator to minimize the error at the end effector and improve the precision of the inverse kinematics solution [3]. The inverse kinematic of redundant manipulators was presented by neural networks (NNs) to obtain the joint angles of the robot using the Cartesian coordinate of the end effector. An inverse kinematic solution was studied by training the neural network with the robot’s end effector Cartesian coordinates and its corresponding joint configurations.
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