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.

翻译：为在社会中高效部署机器人系统，移动机器人需在复杂环境中实现自主安全运动。非线性模型预测控制（MPC）方法为寻找动态可行轨迹提供了一种自然途径，该轨迹既能穿越环境又避免与邻近障碍物碰撞。然而，典型嵌入式机器人系统（如四旋翼飞行器）有限的计算能力对实时运行MPC提出了挑战，尤其涉及计算开销最大的约束生成与优化任务。为解决此问题，我们提出一种新颖的分层MPC架构，包含规划层与跟踪层。规划层以较低频率构建长预测时域的轨迹，而跟踪层则以较高频率确保轨迹跟踪精度。我们证明了所提框架能规避碰撞并保持递归可行性。此外，通过四旋翼飞行器在复杂静态环境中抵达目标位置的仿真与实验室实验，验证了该框架的有效性。代码在四旋翼嵌入式计算机上高效实现，确保了实时可行性。与先进单层MPC方案相比，本方法将规划时域扩展至5倍，从而显著提升了系统性能。