Robotic head neuro-controller based on biologically-inspired neural models

This paper presents the application of a neural approach in the control of a 7-DOF robotic head. The inverse kinematics problem is addressed, for the control of the gaze fixation point of two cameras mounted on the robotic head. The proposed approach is based on a biologically-inspired model, which replicates the human brain capability of creating associations between motor and sensory data, by learning. The model is implemented here by self-organizing neural maps. During learning, the system creates relations between the motor data associated to endogenous movements performed by the robotic head and the sensory consequences of such motor actions, i.e. the final position of the gaze fixation point. The learnt relations are stored in the neural map structure and are then used, after learning, for generating motor commands aimed at reaching a given fixation point. The approach proposed here allows to solve the inverse kinematics and joint redundancy problems for the ARTS robotic head, with good accuracy and robustness. Experimental trials confirmed the system capability to control the gaze direction and fixation point and also to manage the redundancy of the robotic head in reaching the target fixation point even with additional constraints, such as a clamped joint or two symmetric joint angles (e.g. eye joints).