A Hierarchical MPC for End-Effector Tracking Control of Legged Mobile Manipulators

This article presents a hierarchical model predictive control (MPC) framework for the end-effector tracking control problem of a legged mobile manipulator. In the high-level part, a kinematic MPC over a long-time horizon computes both base and joint trajectories, then a quadratic program (QP) based optimization solves ground-reaction-forces (GRFs) satisfying the robot's centroidal dynamics and the friction cone constraints. In the low-level part, a kinodynamic MPC over a short-time horizon tracks command end-effector trajectories and outputs from the high-level part while satisfying all nonlinear dynamics constraints. Due to the complexity of MPC formulations and high real-time requirements, traditional MPC for legged mobile manipulators can only generate short-time horizon solutions for tracking tasks over longer time horizons, which may lead to the optimization falling into bad local minima. In our method, the long-term trajectories from the high-level part can guide the optimization of the short-term kinodynamic MPC to generate a better solution. We validate the effectiveness of our method through several simulation and hardware experiments. In comparison to traditional MPC, the proposed method improves the trajectory tracking accuracy of the robot's end-effector while reducing the violations of the system's physical limit constraints and environment-collision avoidance constraints.