MPC-based motion planning for non-holonomic systems in non-convex domains

TL;DR

This paper develops a new MPC-based motion planning method for non-holonomic robots navigating in complex, non-convex environments, ensuring convergence to targets under realistic conditions.

Contribution

It introduces a novel output tracking MPC formulation that guarantees convergence for non-holonomic systems in non-convex domains, addressing a gap in existing theoretical guarantees.

Findings

Guarantees convergence to target under realistic assumptions

Handles non-holonomic systems in non-convex environments

Provides theoretical proof of completeness

Abstract

Motivated by the application of using model predictive control (MPC) for motion planning of autonomous mobile robots, a form of output tracking MPC for non-holonomic systems and with non-convex constraints is studied. Although the advantages of using MPC for motion planning have been demonstrated in several papers, in most of the available fundamental literature on output tracking MPC it is assumed, often implicitly, that the model is holonomic and generally the state or output constraints must be convex. Thus, in application-oriented publications, empirical results dominate and the topic of proving completeness, in particular under which assumptions the target is always reached, has received comparatively little attention. To address this gap, we present a novel MPC formulation that guarantees convergence to the desired target under realistic assumptions, which can be verified in relevant real-world scenarios.