Jong Jin Park, Seungwon Lee and Benjamin Kuipers. 2017.
Discrete-time dynamic modeling and calibration of differential-drive mobile robots with friction.
IEEE Int. Conf. Robotics and Automation (ICRA), 2017.
Fast and high-fidelity dynamic model is very useful for planning, control, and estimation. Here, we present a fixed-time-step, discrete-time dynamic model of differential drive vehicle with friction for reliable velocity prediction, which is fast, stable, and easy to calibrate.
Unlike existing methods which are predominantly formulated in the continuous-time domain (very often ignoring dry friction) that require numerical solver for digital implementation, our model is formulated directly in a fixed-time-step discrete time setting, which greatly simplifies the implementation and minimizes computational cost. We also explicitly take into account friction, using the stable formulation developed by Kikuuwe [1]. Friction model, while non-trivial to implement, is necessary for predicting wheel locks and velocity steady-states which occur in real physical systems.
In this paper, we present our dynamic model and evaluate it on a physical platform, a commercially-available electric powered wheelchair. We show that our model, which can run over 105 times faster than real-time on a typical laptop, can accurately predict linear and angular velocities without drift. The calibration of our model requires only a time-series of wheel speed measurements (via encoders) and command inputs, making it readily deployable to physical mobile robots.