Embedded Hierarchical MPC for Autonomous Navigation. Benders, D., Köhler, J., Niesten, T., Babuška, R., Alonso-Mora, J., & Ferranti, L. November, 2024. arXiv:2406.11506 [cs]
Embedded Hierarchical MPC for Autonomous Navigation [link]Paper  doi  abstract   bibtex   2 downloads  
To efficiently deploy robotic systems in society, mobile robots need to autonomously and safely move 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.
@misc{benders_embedded_2024,
	title = {Embedded {Hierarchical} {MPC} for {Autonomous} {Navigation}},
	url = {paper=http://arxiv.org/abs/2406.11506},
	doi = {10.48550/arXiv.2406.11506},
	abstract = {To efficiently deploy robotic systems in society, mobile robots need to autonomously and safely move 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.},
	language = {en},
	urldate = {2024-12-02},
	publisher = {arXiv},
	author = {Benders, D. and Köhler, J. and Niesten, T. and Babuška, R. and Alonso-Mora, J. and Ferranti, L.},
	month = nov,
	year = {2024},
	note = {arXiv:2406.11506 [cs]},
	keywords = {Computer Science - Robotics},
}
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