Auditory Feature Driven Model Predictive Control for Sound Source Approaching

다국어 초록 (Multilingual Abstract)

Sound source approaching is a typical task for the robot with auditory sensing. Many existing methods are based on sound source localization (SSL), and utilize the explicit location as the control input. To reduce the localization computation cost and improve the robustness against noise and reverberation, we propose a novel auditory feature driven model predictive control (AFD-MPC) method, which directly uses the auditory feature as the control input. First, a new convolution-ternarization based interaural time difference (CT-ITD) estimation method is proposed, which is more robust to noise and reverberation by eliminating signal spikes and irrelevant components. Second, a new system model is derived and established, which directly links the robot motions and the interaural time difference (ITD) feature. Third, AFD-MPC is realized based on the proposed CT-ITD feature estimation and system model. The states at multiple future time steps are predicted based on the system model, and a control objective function considering both target approaching and motion smoothness is designed. By involving the multi-step future states in the control objective function, the control outcome is more smooth on motion trajectory and more robust to instantaneous interferences. A series of experiments such as static and dynamic sound source approaching are conducted on a mobile robot equipped with a small-sized 6-microphone array to validate the effectiveness of our methods.