Embedded Hierarchical MPC for Autonomous Navigation
<|reference_start|>Embedded Hierarchical MPC for Autonomous Navigation: 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 interconnects 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.<|reference_end|>
@article{benders2024embedded,
    title={Embedded Hierarchical MPC for Autonomous Navigation},
    author={Dennis Benders, Johannes K"ohler, Thijs Niesten, Robert Babuv{s}ka,
  Javier Alonso-Mora and Laura Ferranti},
    journal={arXiv preprint arXiv:2406.11506},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11506},
    primaryClass={cs.RO}
}