Solution for Ill-Posed Inverse Kinematics of Robot Arm by Network Inversion

Takehiko Ogawa,Hajime Kanada

doi:10.1155/2010/870923

Abstract

In the context of controlling a robot arm with multiple joints, the method of estimating the joint angles from the given end-effector coordinates is called inverse kinematics, which is a type of inverse problems. Network inversion has been proposed as a method for solving inverse problems by using a multilayer neural network. In this paper, network inversion is introduced as a method to solve the inverse kinematics problem of a robot arm with multiple joints, where the joint angles are estimated from the given end-effector coordinates. In general, inverse problems are affected by ill-posedness, which implies that the existence, uniqueness, and stability of their solutions are not guaranteed. In this paper, we show the effectiveness of applying network inversion with regularization, by which ill-posedness can be reduced, to the ill-posed inverse kinematics of an actual robot arm with multiple joints.

Highlights

In the context of controlling a robot arm with multiple joints, the problem of estimating the joint angles from the given end-effector coordinates is called the inverse kinematics problem, which is a type of inverse problems [1]
It was introduced as a method to solve the inverse kinematics problem of estimating the multiple joint angles of a robot arm from the given end-effector coordinates [11,12,13]
We examine the inverse estimation of joint angles from the end-effector coordinates of the 3-DOF robot arm, shown in Figure 2(a), using network inversion

Summary

Introduction

In the context of controlling a robot arm with multiple joints, the problem of estimating the joint angles from the given end-effector coordinates is called the inverse kinematics problem, which is a type of inverse problems [1]. Inverse problems are solved by using a trained multilayer neural network inversely to estimate the corresponding input from the given output [7, 8] The advantages of this method are easiness of the direct modeling by learning and adaptive estimation of the inverse solution. It was introduced as a method to solve the inverse kinematics problem of estimating the multiple joint angles of a robot arm from the given end-effector coordinates [11,12,13]

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Results

Conclusion