A real-time tracking and optimised gaze control for a redundant humanoid robot head

Abstract

This paper addresses the problem of gaze control for a humanoid robot head. We propose a control framework comprised of two components: 1) an adaptive visual tracker that is capable of tracking an arbitrary object with unknown trajectory; 2) an optimised visual control strategy capable of controlling all joint motions of a redundant head-neck mechanism in order to retain the tracked object at image centre. The advantage of such a framework is that it does not require any prior knowledge of the object trajectory and can achieve optimal joint motions, i.e. it minimises the maximum joint motions needed to maintain constant gaze. An adaptive gain has been used with the controller in order to provide dynamic convergence of the task space error when gazing at an object with unpredictable or erratic trajectories. The proposed framework has been validated in real-time using our bi-manual humanoid robot platform Boris, and the reported experimental results demonstrate the efficiency of our approach.