In this paper we address the problem of executing fast gaze shifts toward a visual target with a robotic platform. The robotic platform is an anthropomorphic head with seven degrees of freedom (DOFs) that was designed to mimic the physical dimensions (i.e. geometry and masses), the performances (i.e. angles and velocities) and the functional abilities (i.e. neck-movements and eyes vergence) of the human head. In our approach the "gold performance" of the robotic head is represented by the accurate eye-head coordination that is observed during head-free gaze saccades in humans. To this aim, we implemented and tested on the robotic head a well-characterized, biologically inspired model of gaze control and we investigate the effectiveness of the bioinspired paradigm to achieve an appropriate control of the multi-DOF robotic head. Moreover, in order to verify if the proposed model can reproduce the typical patterns of actual human movements, we performed a quantitative investigation of the relation between movement amplitude, duration and peak velocity. In the latter case, we compared the actual robot performances with existing data on human main sequence which is known to provide a general method for quantifying the dynamic of oculomotor control. The obtained results confirmed (1) the ability of the proposed bioinspired control to achieve and maintain and stable fixation of the target which was always well-positioned within the fovea and (2) the ability to reproduce the typical human main sequence diagrams which were never been successfully implemented on a fully anthropomorphic head. Even if fundamentally aimed at the experimental investigation of the underlying neurophysiologic models, the present study is also intended to provide some possible relevant solutions to the development of human-like eye movements in humanoid robots.
|Number of pages||22|
|Publication status||Published - Aug 2008|
- Bioinspired sensory-motor coordination
- Eye-head coordination
- Robotic head
ASJC Scopus subject areas
- Artificial Intelligence