Inverse Kinematics Learning by Modular Architecture Neural Networks

Inverse kinematics computation using an artificial neural network that learns the inverse kinematics of a robot arm has been employed by many researchers. However, conventional learning methodologies do not pay enough attention to the discontinuity of the inverse kinematics system of typical robot arms with joint limits. The inverse kinematics system of the robot arms, including a human arm with a wrist joint, is a multivalued and discontinuous function. Since it is difficult for a well-known multi-layer neural network to approximate such a function, a correct inverse kinematics model for the end-effector's overall position and orientation cannot be obtained by the conventional methods. In order to overcome the drawbacks of the inverse kinematics solver consisting of a single neural network, we propose a novel modular neural network architecture for the inverse kinematics model learning.

Methods for Solving Inverse-kinematics Problems Using Nerual Networks with 0utput Error Feedback

Many studies on the learning control of the robot arm have been conducted by using neural networks, The method that uses an acquired inverse-kinematics model of the arm by learning are popular. However, acquisition of the inverse-kinematics model has a number of drawbacks. Furthermore, a limited scale neural networks system has only limited precision. Errors still remains in the output of the inverse-kinematics model using the neural networks system. In this paper, a new method for solving inverse-kinematics problem using the learned inverse model of the linearized model as output feedback system is proposed. Two possible configurations of the system are presented. The use of linear adaptive systems including Kalman filter is also proposed for higher accuracy. The performances of the proposed methods are shown by numerical simulations

0ff-line Inverse-kinematics Model Learning by an Extended Feedback System

Recently the use of neural networks as the inverse-kinematics model of a robot arm has been proposed in learning control of the robot arm. The forward and inverse modeling, the feedback error leaning schema and the goal directed model inversion were proposed to extend the acquisition of the inverse model for the systems with many-to-one input-output correspondence. However, these methods can be used only for on-line learning. The learning of neural networks usually requires many iterations of robot arm movement and of its position measurement. In order to reduce the number of movements of the robot arm, the hybrid system which consists of a learning element and an extended feedback controller are proposed. The learning element approximates the inverse kinematics model of the robot arm. By using the extended feedback controller, the high precision solutions of the inverse kinematics problems are obtained so that these solutions can be used for the teaching signal of the learning element. After the acquisition of these solutions, the off-line learning of the learning element is conducted. The use of forward model of the robot arm is also proposed. The numerical simulations show the good performance of the proposed system

Model-based image measurement system

A model-based image measurement system which defines a model with variables. measures data, defines measurement equations, solves these equations and estimates values of the variables is proposed. The feasibility of the system is verified by an experimental system that estimates the position, attitude, and inner parameters of an object from its image. Using the numerical method, this system can use measures not expressed by mathematical equations

Development of Assistive Robots Using International Classification of Functioning, Disability, and Health: Concept, Applications, and Issues

Many assistive robots for elderly and disabled people have been developed in the past few decades. However, very few of them became commercially available. The major cause of the problem is that the cost-benefit ratio and the risk-benefit ratio of them are not good or not known. The evaluation of them should be done in the light of the impacts of assistive technologies on users’ whole life, both in short-term and long-term. In this paper, we propose a framework of evaluation and design of assistive robots using ICF (International Classification of Functioning, Disability, and Health). The goal of the framework is the realization of the life design and the improvement of the quality of life using assistive technologies. We describe the concept of utilizing ICF in the development process of assistive robots, and demonstrate its utility by using some examples of practical application such as the analysis of daily living, the design of assistive robots and the evaluation of assistive robots. We also show the issues of using ICF for further development of the framework