Recursive convex replanning for the trajectory tracking of wheeled mobile robots

The article consider the Cartesian trajectory tracking of wheeled mobile robots to be performed by a hybrid control scheme with feedforward inverse control and a state feedback that is only updated periodically and relies on a recursive convex replanning of the reference trajectory. This approach applied to the standard unicycle model is shown to maintain its efficacy also in presence of noise or unmodeled robot dynamics. Explicit, sufficient conditions are provided to ensure global boundedness of the tracking error. Experimental results are presented using Lego Mindstorm mobile robots.