Multi-Sensor Obstacle Detection System Via Model-Based State-Feedback Control in Smart Cane Design for the Visually Challenged

Abstract

Smart canes are usually developed to alert visually challenged users of any obstacles beyond the canes’ physical lengths. The accuracy of the sensors and their actuators’ positions are equally crucial to estimate the locations of the obstacles with respect to the users so as to ensure only correct signals are sent through the associated audio or tactile feedbacks. For implementations with low-cost sensors, however, the users are very likely to get false alerts due to the effects from noise and their erratic readings, and the performance degradation will be more noticeable when the positional fluctuations of the actuators get amplified. In this paper, a multi-sensor obstacle detection system for a smart cane is proposed via a model-based state-feedback control strategy to regulate the detection angle of the sensors and minimize the false alerts to the user. In this approach, the overall system is first restructured into a suitable state-space model, and a linear quadratic regulator (LQR)-based controller is then synthesized to further optimize the actuator’s control actions while ensuring its position tracking. We also integrate dynamic feedback compensators into the design to increase the accuracy of the user alerts. The performance of the resulting feedback system was evaluated via a series of real-time experiments, and we showed that the proposed method provides significant improvements over conventional methods in terms of error reductions.