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.

翻译：为实现机器人系统在社会中的高效部署，移动机器人需具备在复杂环境中自主安全移动的能力。非线性模型预测控制（MPC）方法提供了一种自然的解决方案，可在不碰撞邻近障碍物的前提下，通过环境规划出动态可行的轨迹。然而，典型嵌入式机器人系统（如四旋翼飞行器）的有限计算能力对实时运行MPC提出了挑战，尤其体现在其计算代价最高的两个环节：约束生成与优化求解。针对该问题，本文提出一种新型分层MPC架构，该架构通过规划层与跟踪层的协同工作实现高效控制。规划层以较低频率构建具有长预测时域的轨迹，而跟踪层则以较高频率保证轨迹跟踪精度。我们证明了所提框架能够避免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，验证了该方法的有效性。代码在飞行器嵌入式计算机上高效实现，确保了实时性。与先进单层MPC方案相比，本方法可将规划时域延长5倍，从而显著提升系统性能。