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When designing control strategies for differential-drive mobile robots, onestandard tool is the consideration of a point at a fixed distance along a lineorthogonal to the wheel axis instead of the full pose of the vehicle. Thisabstraction supports replacing the non-holonomic, three-state unicycle modelwith a much simpler two-state single-integrator model (i.e., avelocity-controlled point). Yet this transformation comes at a performancecost, through the robot's precision and maneuverability. This work containsderivations for expressions of these precision and maneuverability costs interms of the transformation's parameters. Furthermore, these costs show thatonly selecting the parameter once over the course of an application may causean undue loss of precision. Model Predictive Control (MPC) represents one suchmethod to ameliorate this condition. However, MPC typically realizes a controlsignal, rather than a parameter, so this work also proposes a Parametric ModelPredictive Control (PMPC) method for parameter and sampling horizonoptimization. Experimental results are presented that demonstrate the effectsof the parameterization on the deployment of algorithms developed for thesingle-integrator model on actual differential-drive mobile robots.

A Parametric MPC Approach to Balancing the Cost of Abstraction for Differential-Drive Mobile Robots