A neuro-controller for robotic manipulators based on biologically-inspired visuo-motor co-ordination neural models

Abstract

This paper presents a novel scheme for sensor-based control of robotics manipulators by means of artificial neural networks. The system is able to control simple reaching tasks by only fusing visual and proprioceptive sensory data, without computational kinematic modeling of the arm structure, Thanks to the generalization features typical of the neural approach, the same neurocontroller has been easily adapted and successfully validated for controlling different manipulators with different mechanical structures, i.e. number of degrees of freedom, link length and weight, etc. The proposed scheme is directly inspired to research results in the field of neuroscience, specifically on nervous structures and physiological mechanisms involved in sensory motor coordination. From a psychological point of view J. Piaget (1976) explained visuo-motor associations in his scheme of circular reaction. He observed how, by making endogenous movements and correlating the resulting arm and hand spatial locations, the brain allows an auto-association to be created between visual and proprioceptive sensing. The work presented in this paper is derived from the more recent DIRECT model proposed D. Bullock et al. (1993). Significant and original modifications of such model have been introduced by the authors to increase, at the same time, both system performance and the biological coherence. The proposed neurocontroller has been first simulated both in the 2-dimensional and the 3-dimensional case, and then implemented for experimental trials on two real robotic manipulators.