Curvature-Aware Model Predictive Contouring Control

Conference paper (2023)

Authors

L. Lyons Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
L. Ferranti Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2023 L. Lyons, L. Ferranti
DOI: https://doi.org/10.1109/ICRA48891.2023.10161177
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Published Date

2023

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

We present a novel Curvature-Aware Model Pre-dictive Contouring Control (CA-MPCC) formulation for mobile robotics motion planning. Our method aims at generalizing the traditional contouring control formulation derived from machining to autonomous driving applications. The proposed controller is able of handling sharp curvatures in the reference path while subject to non-linear constraints, such as lane boundaries and dynamic obstacle collision avoidance. Com-pared to a standard MPCC formulation, our method improves the reliability of the path-following algorithm and simplifies the tuning, while preserving real-time capabilities. We validate our findings in both simulations and experiments on a scaled-down car-like robot.

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