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

2025-10-21

AI Summary

Existing motion planning methods for nonholonomic systems under nonconvex constraints lack theoretical guarantees of convergence. Method: This paper proposes an output-tracking model predictive control (MPC) framework, incorporating slack variables, designing a terminal set and terminal cost tailored to nonholonomic dynamics, and establishing rigorous closed-loop asymptotic convergence and goal reachability under verifiable, realistic assumptions. Contribution/Results: To the best of our knowledge, this is the first MPC-based planning approach that provides theoretical completeness for nonholonomic systems subject to nonconvex constraints. Comprehensive simulations and experiments on canonical nonconvex scenarios demonstrate the method’s feasibility, closed-loop stability, and computational efficiency—thereby bridging a critical gap between empirical practice and theoretical rigor in nonholonomic motion planning.

Technology Category

• Search and Optimization: Non-convex Optimization
• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Security and Privacy: Tracking, profiling, and countermeasures against them
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• MPC motion planning for non-holonomic systems
• Addressing non-convex constraints in output tracking
• Guaranteeing target convergence under realistic assumptions

Innovation

• MPC-based motion planning for non-holonomic systems
• Handles non-convex constraints in motion planning
• Guarantees convergence to target under realistic assumptions