Data-Driven Modeling of a Track-based Stair-Climbing Wheelchair

A stair-climbing wheelchair can notably enhance the autonomy in terms of mobility for the aged and disabled. This paper presents a data-driven system identification approach and a vision-based heading control algorithm for reliable operation of a stair-climbing wheelchair. We develop a track-based wheelchair model and propose a methodology for autonomous stair traversal. Modeling the dynamics of track-based systems is a challenging task. Hence, a data-driven approach based on the Observer Kalman filter Identification/Eigensystem Realization Algorithm is employed for modeling the complex dynamics of the system. For developing an affordable system, low-cost sensors such as Microsoft Kinect and IMU are utilized. We employ a computationally efficient image processing algorithm, and the heading angle is controlled using Linear Quadratic Regulator (LQR). The effectiveness of the proposed methodology for a safe stair traversal is verified in the ROS-Gazebo environment.