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Constructing a smart wheelchair on a commercially available poweredwheelchair (PWC) platform avoids a host of seating, mechanical design andreliability issues but requires methods of predicting and controlling themotion of a device never intended for robotics. Analog joystick inputs aresubject to black-box transformations which may produce intuitive and adaptablemotion control for human operators, but complicate robotic control approaches;furthermore, installation of standard axle mounted odometers on a commercialPWC is difficult. In this work, we present an integrated hardware and softwaresystem for predicting the motion of a commercial PWC platform that does notrequire any physical or electronic modification of the chair beyond plugginginto an industry standard auxiliary input port. This system uses an RGB-Dcamera and an Arduino interface board to capture motion data, including visualodometry and joystick signals, via ROS communication. Future motion ispredicted using an autoregressive sparse Gaussian process model. We evaluatethe proposed system on real-world short-term path prediction experiments.Experimental results demonstrate the system's efficacy when compared to abaseline neural network model.

Learning Motion Predictors for Smart Wheelchair using Autoregressive Sparse Gaussian Process