Control of Voluntary Movements in an Anthropomorphic Robot Finger by Using a Cortical Level Neural Controller

Abstract

Biological control systems have long been studied as possible inspiration for the construction of robotic controllers. In this paper, we present a control of voluntary movements using a cortical network within constraints from neurophysiology. Neural controller is proposed to control desired joint trajectories for multi-joint opponent muscle control of a robot finger. Each joint is controlled by an agonist-antagonist muscle pair. Neural model proposes functional roles for pre-central cortical cell types in the computation of a descending command to spinal alpha and gamma motoneurons. Neurons in anterior area 5 are proposed to compute the position of the link in question using corollary discharges and feedback from muscles spindles. Neurons in posterior area 5 use this position perception to compute a desired movement direction. Through experimental results, we showed that neural controller exhibits key kinematic properties of human movements, dynamics compensation and including asymmetric bell-shaped velocity profiles. Neural controller suggests how the brain may set automatic and volitional gating mechanisms to vary the balance of static and dynamic feedback information to guide the movement command and to compensate for external forces.