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Artificial neural network based robot control: An overview

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Abstract

The current thrust of research in robotics is to build robots which can 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 artificial neural networks. An in-depth overview of the application of neural 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 concentrates on the neural network applications to the motion control of robots involved in both non-contact and contact tasks. The current state of research in this area is surveyed and the strengths and weakness of the present approaches are emphasized. The paper concludes by indentifying areas which need future research work.

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  1. Department of Mechanical Engineering and Materials Science, School of Engineering, Duke University, 27708, Durham, NC, USA

    Sameer M. Prabhu (Graduate Student) & Devendra P. Garg (Professor and ASME Fellow)

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Prabhu, S.M., Garg, D.P. Artificial neural network based robot control: An overview. J Intell Robot Syst 15, 333–365 (1996). https://doi.org/10.1007/BF00437601

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