Embedded Hierarchical MPC for Autonomous Navigation

Journal Article (2025)
Author(s)

Dennis Benders (TU Delft - Learning & Autonomous Control)

Johannes Kohler (ETH Zürich)

T.E.J. Niesten (TU Delft - Learning & Autonomous Control)

Robert Babuska (TU Delft - Learning & Autonomous Control, Czech Technical University)

Javier Alonso-Mora (TU Delft - Learning & Autonomous Control)

L Ferranti (TU Delft - Learning & Autonomous Control)

Research Group
Learning & Autonomous Control
DOI related publication
https://doi.org/10.1109/TRO.2025.3567529
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Publication Year
2025
Language
English
Research Group
Learning & Autonomous Control
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/publishing/publisher-deals Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Volume number
41
Pages (from-to)
3556-3574
Reuse Rights

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Abstract

To efficiently deploy robotic systems in society, mobile robots must move autonomously and safely through complex environments. Nonlinear model predictive control (MPC) methods provide a natural way to find a dynamically feasible trajectory through the environment without colliding with nearby obstacles. However, the limited computation power available on typical embedded robotic systems, such as quadrotors, poses a challenge to running MPC in real time, including its most expensive tasks: constraints generation and optimization. To address this problem, we propose a novel hierarchical MPC scheme that consists of a planning and a tracking layer. The planner constructs a trajectory with a long prediction horizon at a slow rate, while the tracker ensures trajectory tracking at a relatively fast rate. We prove that the proposed framework avoids collisions and is recursively feasible. Furthermore, we demonstrate its effectiveness in simulations and lab experiments with a quadrotor that needs to reach a goal position in a complex static environment. The code is efficiently implemented on the quadrotor's embedded computer to ensure real-time feasibility. Compared to a state-of-the-art single-layer MPC formulation, this allows us to increase the planning horizon by a factor of 5, which results in significantly better performance.

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File under embargo until 06-11-2025