Embedded Hierarchical MPC for Autonomous Navigation

TL;DR

This paper introduces a hierarchical MPC framework for autonomous quadrotor navigation that improves planning horizon and safety in complex environments while ensuring real-time execution on embedded systems.

Contribution

A novel hierarchical MPC scheme with planning and tracking layers that enhances long-horizon planning and real-time feasibility on embedded robotic platforms.

Findings

Hierarchical MPC avoids collisions and is recursively feasible.

The framework increases planning horizon by a factor of 5.

Demonstrated effectiveness in simulations and lab experiments.

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.