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This paper develops a provably stable sensor-driven controller forpath-following applications of robots with unicycle kinematics, one specificclass of which is the wheeled mobile robot (WMR). The sensor measurement isconverted to a scalar value (the score) through some mapping (the scorefunction); the latter may be designed or learned. The score is then mapped toforward and angular velocities using a simple rule with three parameters. Thekey contribution is that the correctness of this controller only relies on thescore function satisfying monotonicity conditions with respect to theunderlying state -- local path coordinates -- instead of achieving specificvalues at all states. The monotonicity conditions may be checked online bymoving the WMR, without state estimation, or offline using a generative modelof measurements such as in a simulator. Our approach provides both thepracticality of a purely measurement-based control and the correctness ofstate-based guarantees. We demonstrate the effectiveness of this path-followingapproach on both a simulated and a physical WMR that use a learned scorefunction derived from a binary classifier trained on real depth images.

Provably Correct Sensor-driven Path-following for Unicycles using Monotonic Score Functions