Navigation framework for humanoid robots integrating gaze control and modified-univector field method to avoid dynamic obstacles

This paper proposes a navigation framework for humanoid robots, which integrates gaze control and modified univector field-based path planning to cope with moving obstacles. To make navigation robust, obstacles are modeled according to their relative velocities and positions. Moreover, partial evaluation values for gaze control architecture are also considered for modifying their virtual size and moving trajectory. In addition, gaze control architecture is proposed, which estimates the size of local map confidence area, self-localization error, surrounding obstacles and obstacle-free distance against those obstacles in the local map. The proposed framework is verified through computer simulations by using a developed simulator for HanSaRam-VIII.