ARTIFICIAL NEURAL-NETWORK-BASED ROBOT CONTROL - AN OVERVIEW

The current thrust of research in robotics is to build robots which ca n operate in dynamic and/or partially known environments. The ability of learning endows the robot with a form of autonomous intelligence to handle such situations. This paper focuses on the intersection of the fields of robot control and learning methods as represented by artifi cial neural networks. An in-depth overview of the application of neura l networks to the problem of robot control is presented. Some typical neural network architectures are discussed first. The important issues involved in the study of robotics are then highlighted. This paper co ncentrates on the neural network applications to the motion control of robots involved in both non-contact and contact tasks. The current st ate of research in this area is surveyed and the strengths and weaknes s of the present approaches are emphasized The paper concludes by inde ntifying areas which need future research work.